Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

Inspired by its recent success, this paper proposes **CheXT**, the first Transformer-based chest X-ray model.

The review comment to be evaluated is:

1) Radiomics is a term that most people in the ICLR community are not familiar with. What is it exactily? Is it a set of radiological/image features or bio-markers in medical images? Or something else? The authors should consider explaining it in plain language or ML language.

2) Prior multi-modal feature fusion work in the chest x-ray domain should be discussed, examples below.

    - Moradi, Mehdi, Ali Madani, Yaniv Gur, Yufan Guo, and Tanveer Syeda-Mahmood. "Bimodal network architectures for automatic generation of image annotation from text." arXiv preprint arXiv:1809.01610 (2018).

    - Wang, Xiaosong, Yifan Peng, Le Lu, Zhiyong Lu, and Ronald M. Summers. "TieNet: Text-Image Embedding Network for Common Thorax Disease Classification and Reporting in Chest X-rays." arXiv e-prints (2018): arXiv-1801.

    - Xue, Yuan, and Xiaolei Huang. "Improved disease classification in chest x-rays with transferred features from report generation." In International Conference on Information Processing in Medical Imaging, pp. 125-138. Springer, Cham, 2019.

    - Liao, Ruizhi, Daniel Moyer, Miriam Cha, Keegan Quigley, Seth Berkowitz, Steven Horng, Polina Golland, and William M. Wells. "Multimodal Representation Learning via Maximization of Local Mutual Information." arXiv preprint arXiv:2103.04537 (2021).

3) I don't see "prior knowledge" and how it's utilized in this work. Isn't the so called prior knowledge in this paper essentially extracted from the data (chest x-ray images)?

4) What are the patch sizes? How are they determined?

5) There are 14 pathology labels in the NIH chest x-ray dataset. Why/how did the authors select the 8 classes out of the 14?

6) In Table 1, make the best results bold instead of in red.

7) Are the AUC values generated on the test set? If so, I'm concerned about overfitting by over tuning the hyper-parameters.

The evidence to be evaluated is:Recently, Vision Transformer (ViT) (Dosovitskiy et al., 2020) achieved state-of-the-art classification on ImageNet by directly applying Transformers with global self-attention to full-sized images. Inspired by the promising performance of ViT, researchers have recently applied the idea to medical images.

(Park et al., 2021) utilized a hybrid framework of CNN and Transformer for Covid-19 prediction.

An overview of CheXT is illustrated in Figure 2. In the following subsections, we will first present Cross-attention Vision Transformer (CrossViT), a recent two-branch ViT backbone on which CheXT is built, and then describe our many unique improvements customized for Chest X-ray analysis. ViT first converts an image into a sequence of patch tokens by dividing the image with certain patch size and linearly projecting each patch into tokens. A special token (CLS) is added in front of the sequence, as in the original BERT (Devlin et al., 2018). Then, all tokens are passed through stacked Transformer encoders. Finally, the hidden state corresponding to the CLS token is used as the aggregate sequence representation for image classification. A Transformer encoder is composed of a sequence of blocks where each block contains multiheaded self-attention with a feed-forward network.

In CheXT, we extend their idea by treating image itself as one "view" and the radiomic feature extracted from the same image as another "view" (Figure 2). The two views are the jointly learned by interacting through cross-attention. Transformer serves as the modality-agnostic backbone for both. Specifically, we introduce a dual-branch cross-attention Transformer where the first (primary) branch operates the image part, while the second (complementary) branch handles the radiomic features. To resolve the "chicken-and-egg" dilemma in extracting reliable radiomic features without bounding boxes, we have designed a novel _Bootstrap Your Own Attention_ (**BYOA**) module, using feedbacks to learn region localization for extracting radiomic features. A simple yet effective module is also utilized to fuse information between the branches. In the subsequent sections, we will describe the two branches, the BOYA module, and the fusion module.

In our framework, we aim to make the hidden features of the CLS similar to the radiomic features to learn the localization of pathologies in the chest X-rays. For this branch, we use the vanilla Transformer (Liu et al., 2017) as the radiomic features encoder.

As shown in Figure 2, CheXT is trained using the linear combination of the supervised focal classification and unsupervised contrastive losses. For the supervised classification, considering that the chest X-ray dataset is usually highly imbalanced, we adopt the focal loss (Pasupa et al., 2020). For unsupervised contrastive learning, we use the cross-view contrastive loss (Chen et al., 2020).

We feed the output of the CLS tokens of two branches \(I_{cls}\) and \(R_{cls}\) to a simple linear classifier.

Not only we compared CheXT with previous CNN-based SOTA models, but also several Transformer-based models.

Compared to all baselines, CheXT achieves a mean AUC score of 0.839 using a dual-branch shallow Transformer across the 8 different diseases, which is 0.011 higher than the SOTA (uses DenseNet-121) (Rajpurkar et al., 2017) on disease classification. Specifically, our results significantly outperformed the best baseline models by 0.13, 0.09, and 0.02 in AUC for detecting Infiltration, Nodule, and Mass higher respectively. Besides, compared to the Transformer-based models, the key difference is that we utilize the extracted radiomic features for disease prediction, which improves the accuracy and adds the model's interpretability due to the utilization of self-designed handcrafted radiomic features.

In a calculated ratio, they help CheXT to learn both disease-level and patient-level discriminative visual features.

In this paper, we propose an end-to-end knowledge-guided cross-attention Transformer, named CheXT that can jointly model abnormality classification and localization without the supervision of localization annotation of chest X-rays. Our approach differs from previous studies in the choice of universal modeling transformer, the use of radiomic features as prior knowledge, and a feedback loop for image and radiomic features to mutually interact with each other.

Input: Figure 2: Overview of our proposed CheXT. It contains two branches, the Image branch and the Radiomics branch, to process the image and radiomic features (generated by the BYOA module shown in Figure 3), respectively. The output tokens are then fused by an efficient module via cross attention of the CLS tokens. Finally, the output of two CLS tokens (Icls andRcls) are used for disease classification. We minimize the classification errors via a focal loss. Since we train with both labeled and unlabeled images, we leverage a contrastive learning strategy. Specifically, we generate the image view zi = gi(Icls) by a projection head gi. Similarily, we generate the radiomic view zr = gr(Rcls) by another projection head gr. We maximize the agreement between zi and zr via a contrastive loss (NT-Xent). Of note, the contrastive loss is only active during the training.

Input: Figure 3: Overview of our BYOA module. For the input chest X-rays, we look at the self-attention of the CLS token of the Image branch on the heads of the final output of the cross attention module. Then we apply a threshold (0.1) (which means we only keep the top 10% active pixels in the whole attention map) to generate the bounding boxes. Then with the generated bounding boxes, we use the Pyradiomic tool as the radiomic extractor to extract the radiomic features.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the claim and evidence, I find that stating CheXT as "the first Transformer-based chest X-ray model" is overstated. The evidence itself mentions other Transformer-based approaches that preceded or were concurrent with this work, such as Park et al. (2021) who "utilized a hybrid framework of CNN and Transformer for Covid-19 prediction." Additionally, the reviewer points out several prior multi-modal works in the chest x-ray domain. While CheXT introduces novel elements like the dual-branch cross-attention and BYOA module, claiming it as "the first" is not accurate based on the available evidence.

<score>4</score>

Justification:
1. The evidence directly contradicts the "first" claim by mentioning Park et al.'s prior Transformer work
2. The reviewer lists multiple prior works in chest x-ray analysis
3. The authors' own literature review acknowledges that "researchers have recently applied the [Transformer] idea to medical images"
4. The novelty appears to be in specific architectural choices (dual-branch, BYOA) rather than being the first Transformer application to chest x-rays
5. A more accurate claim would focus on CheXT's specific innovations rather than claiming to be the first Transformer-based model


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

Inspired by its recent success, this paper proposes **CheXT**, the first Transformer-based chest X-ray model.

The review comment to be evaluated is:

Strength:
- The proposed approach and considering radiomic features along with learnt features is valuable and very relevant to the community.

Main Concerns and comments to improve the paper:
- The paper does not consider evaluation of the method on more publicly available datasets and the study is only limited to NIH-14 Chest X-ray data.
- Baselines and state of the art results on the test data: One important baseline is to make the prediction using only radiomic features.  Also,  [Han’20]  has reported much higher performance on NIH-14 and for the same task which they have not been included in the paper.
Improvement over the CNN baseline on classification: We see 0.011 improvement in mean AUC over Rajpurkar’17 as one of the baseline which is not significant.
- Details of the comparison setups are missing. I am wondering if these numbers are collected from the reported values by authors or they ran every SOTA against their data division (less likely since there is no error bound reported for SOTAs). And if they are-implemented or re-evaluated each method what are the details and consideration for a fair comparison and reproducing the results.
- Main contribution of the paper is to activate usage or radiomic features within the blackbox  structure of deep networks, however, there is no discussion or insight around the meaning and importance of multiple radiomic features that have been used. There are also works that include prior knowledge for Chest X-ray classification including [Han’20]. These features either unlock a better performance (which seems not being really successful in) or demystify the learnt feature space in a way that is pleasant for human interpreters. The paper does not provide such results.

Suggestion to improve the work:
- It would be useful to add some more explanation around radiomics term, feature extraction procedure and meaning of it, since this is not a common term for the machine learning community.
- Consider boosting the results and observation using a few more test data such as MIMIC and CheXpert that have the similar label space as NIH-14 and publicly being used in most of the related papers.
- The above mentioned datasets also can be used for evaluation of the out-of-domain generalization of the method which has not been discussed in the paper.
- Experimental setup and evaluation toward the SOTA can improve significantly. For the comparison purpose I would consider producing a statistical error range with either k-fold cross validation or boot-strapping for both CheXT and SOTAs. I would personally see a proper statistical comparison between a single best SOTA with the proposed method more valuable than having multiple models without any statistical validation.
- Adding interpretation around radiomic feature meaning, how the proposed method unlocked one or more of the 3 listed limitations of CNN methods that has been discussed in the Introduction.
- It is worth also discussing failure cases and the reasoning behind lower performance for some of the conditions such as effusion and pneumonia.

[Han'20] Han, Yan, Chongyan Chen, Liyan Tang, Mingquan Lin, Ajay Jaiswal, Song Wang, Ahmed Tewfik, George Shih, Ying Ding, and Yifan Peng. "Using Radiomics as Prior Knowledge for Thorax Disease Classification and Localization in Chest X-rays." arXiv preprint arXiv:2011.12506 (2020).

The evidence to be evaluated is:Recently, Vision Transformer (ViT) (Dosovitskiy et al., 2020) achieved state-of-the-art classification on ImageNet by directly applying Transformers with global self-attention to full-sized images. Inspired by the promising performance of ViT, researchers have recently applied the idea to medical images.

(Park et al., 2021) utilized a hybrid framework of CNN and Transformer for Covid-19 prediction.

An overview of CheXT is illustrated in Figure 2. In the following subsections, we will first present Cross-attention Vision Transformer (CrossViT), a recent two-branch ViT backbone on which CheXT is built, and then describe our many unique improvements customized for Chest X-ray analysis. ViT first converts an image into a sequence of patch tokens by dividing the image with certain patch size and linearly projecting each patch into tokens. A special token (CLS) is added in front of the sequence, as in the original BERT (Devlin et al., 2018). Then, all tokens are passed through stacked Transformer encoders. Finally, the hidden state corresponding to the CLS token is used as the aggregate sequence representation for image classification. A Transformer encoder is composed of a sequence of blocks where each block contains multiheaded self-attention with a feed-forward network.

In CheXT, we extend their idea by treating image itself as one "view" and the radiomic feature extracted from the same image as another "view" (Figure 2). The two views are the jointly learned by interacting through cross-attention. Transformer serves as the modality-agnostic backbone for both. Specifically, we introduce a dual-branch cross-attention Transformer where the first (primary) branch operates the image part, while the second (complementary) branch handles the radiomic features. To resolve the "chicken-and-egg" dilemma in extracting reliable radiomic features without bounding boxes, we have designed a novel _Bootstrap Your Own Attention_ (**BYOA**) module, using feedbacks to learn region localization for extracting radiomic features. A simple yet effective module is also utilized to fuse information between the branches. In the subsequent sections, we will describe the two branches, the BOYA module, and the fusion module.

In our framework, we aim to make the hidden features of the CLS similar to the radiomic features to learn the localization of pathologies in the chest X-rays. For this branch, we use the vanilla Transformer (Liu et al., 2017) as the radiomic features encoder.

As shown in Figure 2, CheXT is trained using the linear combination of the supervised focal classification and unsupervised contrastive losses. For the supervised classification, considering that the chest X-ray dataset is usually highly imbalanced, we adopt the focal loss (Pasupa et al., 2020). For unsupervised contrastive learning, we use the cross-view contrastive loss (Chen et al., 2020).

We feed the output of the CLS tokens of two branches \(I_{cls}\) and \(R_{cls}\) to a simple linear classifier.

Not only we compared CheXT with previous CNN-based SOTA models, but also several Transformer-based models.

Compared to all baselines, CheXT achieves a mean AUC score of 0.839 using a dual-branch shallow Transformer across the 8 different diseases, which is 0.011 higher than the SOTA (uses DenseNet-121) (Rajpurkar et al., 2017) on disease classification. Specifically, our results significantly outperformed the best baseline models by 0.13, 0.09, and 0.02 in AUC for detecting Infiltration, Nodule, and Mass higher respectively. Besides, compared to the Transformer-based models, the key difference is that we utilize the extracted radiomic features for disease prediction, which improves the accuracy and adds the model's interpretability due to the utilization of self-designed handcrafted radiomic features.

In a calculated ratio, they help CheXT to learn both disease-level and patient-level discriminative visual features.

In this paper, we propose an end-to-end knowledge-guided cross-attention Transformer, named CheXT that can jointly model abnormality classification and localization without the supervision of localization annotation of chest X-rays. Our approach differs from previous studies in the choice of universal modeling transformer, the use of radiomic features as prior knowledge, and a feedback loop for image and radiomic features to mutually interact with each other.

Input: Figure 2: Overview of our proposed CheXT. It contains two branches, the Image branch and the Radiomics branch, to process the image and radiomic features (generated by the BYOA module shown in Figure 3), respectively. The output tokens are then fused by an efficient module via cross attention of the CLS tokens. Finally, the output of two CLS tokens (Icls andRcls) are used for disease classification. We minimize the classification errors via a focal loss. Since we train with both labeled and unlabeled images, we leverage a contrastive learning strategy. Specifically, we generate the image view zi = gi(Icls) by a projection head gi. Similarily, we generate the radiomic view zr = gr(Rcls) by another projection head gr. We maximize the agreement between zi and zr via a contrastive loss (NT-Xent). Of note, the contrastive loss is only active during the training.

Input: Figure 3: Overview of our BYOA module. For the input chest X-rays, we look at the self-attention of the CLS token of the Image branch on the heads of the final output of the cross attention module. Then we apply a threshold (0.1) (which means we only keep the top 10% active pixels in the whole attention map) to generate the bounding boxes. Then with the generated bounding boxes, we use the Pyradiomic tool as the radiomic extractor to extract the radiomic features.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the claim, evidence, and reviewer comments, I'll evaluate whether the claim "CheXT is the first Transformer-based chest X-ray model" is well-stated or overstated.

Summary:
The claim appears to be overstated based on both the evidence and reviewer comments. The paper's own literature review mentions that Park et al. (2021) had already utilized a hybrid CNN-Transformer framework for Covid-19 prediction in chest X-rays. Additionally, the reviewer points out that there are other works incorporating similar approaches, such as Han'20. While CheXT may introduce novel elements like the dual-branch architecture and radiomic features, claiming to be "the first" Transformer-based chest X-ray model is not supported by the evidence.

<score>4</score>

The score of 4 indicates significant overstatement because:
1. The evidence directly contradicts the "first" claim by citing prior Transformer use in chest X-rays
2. The reviewer comments highlight existing work in this space
3. The paper's actual innovation appears to be in the specific architecture and integration of radiomic features, not in being the first to apply Transformers to chest X-rays

A more accurate claim would focus on CheXT's specific innovations rather than claiming to be the first Transformer-based model for chest X-rays.


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

CheXT targets (semi-supervised) abnormality classification and localization from chest X-rays, enhanced by baked-in auxiliary knowledge guidance using radiomics.

The evidence to be evaluated is:The design of radiomics involves biological and medical data and prior knowledge. Thus, radiomics vastly enriches images and expands the horizons of the image toward in-vivo biologic information extraction (Gillies et al., 2016). In image-based biomarkers for cancer staging and prognostication, radiomics had shown promising power (Nasief et al., 2019). Radiomics extracts quantitative data from medical images to represent tumor phenotypes, such as spatial heterogeneity of a tumor and spatial response variations.

(Huang et al., 2018) showed that radiomics could increase the positive predictive value and reduce the false-positive rate in lung cancer screening for small nodules compared with radiologists.

We believe that the joint utilization of radiomics and interpretable learning techniques in our framework can further advance accurate yet interpretable learning in the medical image domain.

(Han et al., 2021) applied pre-extracted radiomic features to guide pneumonia detection from chest X-ray images.

An overview of CheXT is illustrated in Figure 2. In the following subsections, we will first present Cross-attention Vision Transformer (CrossViT), a recent two-branch ViT backbone on which CheXT is built, and then describe our many unique improvements customized for Chest X-ray analysis.

In CheXT, we extend their idea by treating image itself as one "view" and the radiomic feature extracted from the same image as another "view" (Figure 2). The two views are the jointly learned by interacting through cross-attention. Transformer serves as the modality-agnostic backbone for both. Specifically, we introduce a dual-branch cross-attention Transformer where the first (primary) branch operates the image part, while the second (complementary) branch handles the radiomic features. To resolve the "chicken-and-egg" dilemma in extracting reliable radiomic features without bounding boxes, we have designed a novel _Bootstrap Your Own Attention_ (**BYOA**) module, using feedbacks to learn region localization for extracting radiomic features.

The primary image branch uses a Progress-Sampling ViT (PS-ViT) (Yue et al., 2021) as its backbone. Unlike the vanilla ViT that splits images into fixed-size tokens, PS-ViT utilizes an iterative and progressive sampling strategy to locate discriminative regions and avoid over-partition object structures. We experimentally observed PS-ViT outperforms ViT and other variants in our framework because it generates higher-quality and more structure-aware attention maps, which are crucial for estimating the pathology localization during training. **Radiomics Branch.** The complementary radiomics branch is for processing radiomic features. Handcrafted features usually cover a wide range of categories, such as first-order (basic intensity and shaped-based features), second-order (texture features extracted from various matrices), and more advanced features including those calculated from Fourier and wavelet transforms. Specifically, the radiomic features are composed of the following categories:* First-Order statistics features to measure the distribution of voxel intensities within the bounding boxes. The features include energy (the measurement of the magnitude of voxel values), entropy (the measurement of uncertainty in the image values), and max/mean/median gray level intensity within the region of interest.

* Shape-based features, such as Mesh Surface, Pixel Surface, and Perimeter.

* Gray-level features, such as Gray Level Co-occurrence Matrix (GLCM), Gray Level Size Zone (GLSZM), Gray Level Run Length Matrix (GLRLM), Neighboring Gray Tone Difference Matrix (NGTDM), and Gray Level Dependence Matrix (GLDM) features. In short, radiomic features are a set of quantitative features that can describe the characteristics of medical images. In our framework, we aim to make the hidden features of the CLS similar to the radiomic features to learn the localization of pathologies in the chest X-rays. For this branch, we use the vanilla Transformer (Liu et al., 2017) as the radiomic features encoder.

Our main _roadblock_ is how to generate robust radiomic features without pathology localization. On one hand, radiomic features are dependent on and highly sensitive to the local image regions of interest, for which we have no bounding box annotation. On the other hand, image features would benefit from the guidance from radiomic features that encode important domain knowledge. The learning of image and radiomic features are fully entangled, forming a challenging "chicken-and-egg" loop. To address this issue, we design **BYOA** to constitute an end-to-end feedback loop that can bootstrap accurate pathology localization from image data, without any bounding box used for training (Figure 3). BYOA contains two components: attention map generation and radiomic feature extraction.

Then we apply a threshold on the self-attention maps to generate bounding boxes for the extraction of radiomic features. The choice of the threshold will influence the quality of the radiomic features. Specifically, the threshold is designed as how much active pixels to keep in the generated attention map. The more active pixels we keep, the larger the generated bounding box is.

Given the original images and generated bounding boxes, we used the Pyradiomic tool to extract radiomic features (Van Griethuysen et al., 2017).

As shown in Figure 2, CheXT is trained using the linear combination of the supervised focal classification and unsupervised contrastive losses. For the supervised classification, considering that the chest X-ray dataset is usually highly imbalanced, we adopt the focal loss (Pasupa et al., 2020). For unsupervised contrastive learning, we use the cross-view contrastive loss (Chen et al., 2020).

We feed the output of the CLS tokens of two branches \(I_{cls}\) and \(R_{cls}\) to a simple linear classifier.

Our contrastive loss extends the normalized temperature scaled cross-entropy loss (NT-Xent). The difference is that we maximize agreement between two feature views extracted from different input formats, one the image and the other radiomics. Given an anchor chest X-ray in a minibatch, the positive sample will be its radiomic feature view, and the negative samples will be other chest X-rays (either image or radiomics). Since the CLS could be regarded as the representation of all other tokens, we only need to maximize the agreement between them. Suppose \(I_{cls,k}\) and \(R_{cls,k}\) are the \(k-\)th image features and radiomic features in the minibatch, respectively, and \(sim()\) the cosine similarity, the loss function \(\mathcal{L}_{cl}\) is defined as \[\mathcal{L}_{cl}=-\log\frac{\exp(sim(g_{i}(I_{cls,k}),g_{r}(R_{cls,k}))/\tau) }{\sum_{k=1}^{N}\exp(sim(g_{i}(I_{cls,k}),g_{r}(R_{cls,k}))/\tau)} \tag{2}\]

Overall, we treat CheXT training as a semi-supervised multi-task learning. Since for chest x-rays, there exists two labels, disease class labels and pathology bounding box annotations. In our case, we only have access to the disease labels, however, bounding box annotations are more important than the disease class labels for chest x-rays. Here, when we say "semi-supervised", we refer to the bounding box annotations, not disease class labels. For multi-task, one task is supervised disease classification. The other is unsupervised cross-view contrastive learning.

The NIH dataset also includes high-quality bounding box annotations for 880 images by radiologists.

A significant difference between our method and existing baseline methods (Liu et al., 2019; Li et al., 2018) is that our method does not require any training data related to the bounding box while others use some percentage of these images for training.

We build our image branch encoder based on PS-ViT (Yue et al., 2021) and apply their default hyperparameters for training. The only difference is that we use a more shallow PS-ViT with 6 layers. For the radiomic branch encoder, since the radiomic features are already informative features, we use a more shallow Transformer (1-layers) to encode the radiomic features.

Abnormality classification is a multi-label classification problem. It assigns one or more labels among 8 cardiopulmonary diseases (Atelectasis, Cardiomegaly, Effusion, Infiltration, Mass, Nodule, Pneumonia, and Pneumothorax) to each input image at inference time. We conducted 3-fold cross-validation (Table 1). We compared CheXT with reference models, which have published state-of-the-art performance of disease classification on the NIH Chest X-ray dataset.

Compared to all baselines, CheXT achieves a mean AUC score of 0.839 using a dual-branch shallow Transformer across the 8 different diseases, which is 0.011 higher than the SOTA (uses DenseNet-121) (Rajpurkar et al., 2017) on disease classification. Specifically, our results significantly outperformed the best baseline models by 0.13, 0.09, and 0.02 in AUC for detecting Infiltration, Nodule, and Mass higher respectively. Besides, compared to the Transformer-based models, the key difference is that we utilize the extracted radiomic features for disease prediction, which improves the accuracy and adds the model's interpretability due to the utilization of self-designed handcrafted radiomic features.

During our experiments, we found that CheXT performs worse when very large bounding boxes are generated. This observation meets our expectations since radiomic features are very subtle to bounding boxes. Specifically, the extracted radiomic features are more robust if more focal and accurate bounding boxes are given.

In a calculated ratio, they help CheXT to learn both disease-level and patient-level discriminative visual features. The NIH Chest X-ray dataset has 880 images labeled by radiologists with the bounding box information. We have used this dataset to evaluate the performance of CheXT for abnormality localization.

For localization, we evaluated our detected regular rectangular regions against the annotated bounding boxes, using intersection over union ratio (IoU).

CheXT average performance across 8 diseases is significantly better than the baseline under all IoU thresholds. When the thresholds of IoU is set to 0.1, CheXT outperforms the baseline (Wang et al., 2017) in Cardiomegaly, infiltration, Mass, and Pneumonia. Even with higher thresholds, our model performs superior to baseline. For example, when evaluated at T(IoU) = 0.5, our "Cardiomegaly" accuracy is still 32%, while the reference model achieves only 18%.

More importantly, we also add the ViT as our additional baseline here, the results show that the radiomics branch and BYOA module can help the model learn more important regions, which also improves the model's interpretability.

In this paper, we propose an end-to-end knowledge-guided cross-attention Transformer, named CheXT that can jointly model abnormality classification and localization without the supervision of localization annotation of chest X-rays. Our approach differs from previous studies in the choice of universal modeling transformer, the use of radiomic features as prior knowledge, and a feedback loop for image and radiomic features to mutually interact with each other. Additionally, the project aims to mitigate current gaps in radiology by making prior knowledge more accessible to image data analytic and diagnostic assisting tools, with the hope that this will increase the model's interpretability. Experimental results demonstrate that our method outperforms the state-of-the-art algorithms, especially for the disease localization task, where our method can generate more accurate bounding boxes.

Input: Table 1: Comparison with the baseline models for AUC of each class and average AUC. For each column, bold values denote the best results.

Input: Figure 2: Overview of our proposed CheXT. It contains two branches, the Image branch and the Radiomics branch, to process the image and radiomic features (generated by the BYOA module shown in Figure 3), respectively. The output tokens are then fused by an efficient module via cross attention of the CLS tokens. Finally, the output of two CLS tokens (Icls andRcls) are used for disease classification. We minimize the classification errors via a focal loss. Since we train with both labeled and unlabeled images, we leverage a contrastive learning strategy. Specifically, we generate the image view zi = gi(Icls) by a projection head gi. Similarily, we generate the radiomic view zr = gr(Rcls) by another projection head gr. We maximize the agreement between zi and zr via a contrastive loss (NT-Xent). Of note, the contrastive loss is only active during the training.

Input: Figure 3: Overview of our BYOA module. For the input chest X-rays, we look at the self-attention of the CLS token of the Image branch on the heads of the final output of the cross attention module. Then we apply a threshold (0.1) (which means we only keep the top 10% active pixels in the whole attention map) to generate the bounding boxes. Then with the generated bounding boxes, we use the Pyradiomic tool as the radiomic extractor to extract the radiomic features.

Input: Figure 1: General overview of our CheXT framework for Cardiopulmonary Disease Classification and Localization from chest X-rays. CheXT takes the chest X-ray image as the input and outputs a heatmap for pathology localization, based on which the bounding box could be obtained. Radiomic features are further extracted from the bounded region and are fed to predict the disease.

Input: Figure 5: Examples of visualization of localization on the test images. The attention maps are generated from the self-attention maps of the CLS token. The ground-truth bounding boxes are shown in blue. The left image in each pair is the localization result of ViT (Dosovitskiy et al., 2020). The right one is our localization results. All examples are positive for corresponding disease labels. Best viewed in color.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the evidence, I find that the claim is generally well-stated and accurately reflects the capabilities and implementation of CheXT. The evidence demonstrates:

1. The use of semi-supervised learning (only disease labels, no bounding box annotations for training)
2. Clear abnormality classification and localization capabilities (supported by AUC scores and IoU metrics)
3. Integration of radiomics through the BYOA module and radiomics branch, with detailed explanation of radiomic feature extraction
4. Evidence of performance improvements over baselines, particularly in localization tasks

The claim avoids exaggeration and clearly states the core functionalities that are thoroughly supported by the methodology and results presented.

<score>1</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

CheXT targets (semi-supervised) abnormality classification and localization from chest X-rays, enhanced by baked-in auxiliary knowledge guidance using radiomics.

The review comment to be evaluated is:

Strengths
+ The paper is fairly well written and presents a well-studied literature review.
+ The paper proposes an end-to-end transformer-based disease classification and localization model incorporating radiomic feature knowledge with cross-attention. The idea of bounding box generation and radiomic feature extraction in a feedback loop module is new and interesting.
+ Experimental setting including the ablation study for the effect of thresholds on attention maps and the effect of contrastive learning is good.

Weaknesses
- Exposition of results is not good. CheXT is compared against some weak baselines. A more appropriate CNN baseline should be something that includes similar radiomic feature extraction and bounding box generation [1]. Also, [1] is not cited in Related Work.
- The paper claims significant performance improvement by the CheXT model although no evidence (statistical test) is provided to support the claim.
-  Classification performance drops for some of the disease classes (effusion, pneumonia, pneumothorax) after incorporating the radiomics branch. It looks like radiomics branch is hurting the performance for these classes. Is there any impact of label distribution?
- In Fig. 3, two images look different. Why so?
- The training of CheXT is not necessarily semi-supervised as every image has at least disease class label.
- It is not clear how to obtain f_i and f_r; y, y' are not defined Eqn. (1).
- It would be more viable to report both classification and localization performances on the same 880 images. Is there any correlation between e.g., classification AUC and localization IoU?
- It is not clear how the model could improve generalization. There is no generalization assessment of the model for out-of-distribution cases. For example, using other datasets such as, CheXpert and MIMIC-CXR.
- In 4.1, the paper mentions the train/test/val splits, but Table 1 reports results after 3-fold cross-validation. They don't look consistent.
- In 4.4.2: "when the IoU is set to 0.1" should be threshold of IoU. "For Cardiomegaly, Infiltration, and Pneumonia." incomplete sentence.
- It is not clear how higher values of \lambda implies lower weight to contrastive loss in Eqn. (3). How does the model perform without the contrastive loss at all (\lambda=0)?
- There is no mention of the execution speed, computing resource, and failure cases (if any) for the proposed model.
- There are certainly other Transformer-based chest X-ray models such as [2].

1. https://arxiv.org/abs/2011.12506
2. https://arxiv.org/abs/2104.07235

The evidence to be evaluated is:The design of radiomics involves biological and medical data and prior knowledge. Thus, radiomics vastly enriches images and expands the horizons of the image toward in-vivo biologic information extraction (Gillies et al., 2016). In image-based biomarkers for cancer staging and prognostication, radiomics had shown promising power (Nasief et al., 2019). Radiomics extracts quantitative data from medical images to represent tumor phenotypes, such as spatial heterogeneity of a tumor and spatial response variations.

(Huang et al., 2018) showed that radiomics could increase the positive predictive value and reduce the false-positive rate in lung cancer screening for small nodules compared with radiologists.

We believe that the joint utilization of radiomics and interpretable learning techniques in our framework can further advance accurate yet interpretable learning in the medical image domain.

(Han et al., 2021) applied pre-extracted radiomic features to guide pneumonia detection from chest X-ray images.

An overview of CheXT is illustrated in Figure 2. In the following subsections, we will first present Cross-attention Vision Transformer (CrossViT), a recent two-branch ViT backbone on which CheXT is built, and then describe our many unique improvements customized for Chest X-ray analysis.

In CheXT, we extend their idea by treating image itself as one "view" and the radiomic feature extracted from the same image as another "view" (Figure 2). The two views are the jointly learned by interacting through cross-attention. Transformer serves as the modality-agnostic backbone for both. Specifically, we introduce a dual-branch cross-attention Transformer where the first (primary) branch operates the image part, while the second (complementary) branch handles the radiomic features. To resolve the "chicken-and-egg" dilemma in extracting reliable radiomic features without bounding boxes, we have designed a novel _Bootstrap Your Own Attention_ (**BYOA**) module, using feedbacks to learn region localization for extracting radiomic features.

The primary image branch uses a Progress-Sampling ViT (PS-ViT) (Yue et al., 2021) as its backbone. Unlike the vanilla ViT that splits images into fixed-size tokens, PS-ViT utilizes an iterative and progressive sampling strategy to locate discriminative regions and avoid over-partition object structures. We experimentally observed PS-ViT outperforms ViT and other variants in our framework because it generates higher-quality and more structure-aware attention maps, which are crucial for estimating the pathology localization during training. **Radiomics Branch.** The complementary radiomics branch is for processing radiomic features. Handcrafted features usually cover a wide range of categories, such as first-order (basic intensity and shaped-based features), second-order (texture features extracted from various matrices), and more advanced features including those calculated from Fourier and wavelet transforms. Specifically, the radiomic features are composed of the following categories:* First-Order statistics features to measure the distribution of voxel intensities within the bounding boxes. The features include energy (the measurement of the magnitude of voxel values), entropy (the measurement of uncertainty in the image values), and max/mean/median gray level intensity within the region of interest.

* Shape-based features, such as Mesh Surface, Pixel Surface, and Perimeter.

* Gray-level features, such as Gray Level Co-occurrence Matrix (GLCM), Gray Level Size Zone (GLSZM), Gray Level Run Length Matrix (GLRLM), Neighboring Gray Tone Difference Matrix (NGTDM), and Gray Level Dependence Matrix (GLDM) features. In short, radiomic features are a set of quantitative features that can describe the characteristics of medical images. In our framework, we aim to make the hidden features of the CLS similar to the radiomic features to learn the localization of pathologies in the chest X-rays. For this branch, we use the vanilla Transformer (Liu et al., 2017) as the radiomic features encoder.

Our main _roadblock_ is how to generate robust radiomic features without pathology localization. On one hand, radiomic features are dependent on and highly sensitive to the local image regions of interest, for which we have no bounding box annotation. On the other hand, image features would benefit from the guidance from radiomic features that encode important domain knowledge. The learning of image and radiomic features are fully entangled, forming a challenging "chicken-and-egg" loop. To address this issue, we design **BYOA** to constitute an end-to-end feedback loop that can bootstrap accurate pathology localization from image data, without any bounding box used for training (Figure 3). BYOA contains two components: attention map generation and radiomic feature extraction.

Then we apply a threshold on the self-attention maps to generate bounding boxes for the extraction of radiomic features. The choice of the threshold will influence the quality of the radiomic features. Specifically, the threshold is designed as how much active pixels to keep in the generated attention map. The more active pixels we keep, the larger the generated bounding box is.

Given the original images and generated bounding boxes, we used the Pyradiomic tool to extract radiomic features (Van Griethuysen et al., 2017).

As shown in Figure 2, CheXT is trained using the linear combination of the supervised focal classification and unsupervised contrastive losses. For the supervised classification, considering that the chest X-ray dataset is usually highly imbalanced, we adopt the focal loss (Pasupa et al., 2020). For unsupervised contrastive learning, we use the cross-view contrastive loss (Chen et al., 2020).

We feed the output of the CLS tokens of two branches \(I_{cls}\) and \(R_{cls}\) to a simple linear classifier.

Our contrastive loss extends the normalized temperature scaled cross-entropy loss (NT-Xent). The difference is that we maximize agreement between two feature views extracted from different input formats, one the image and the other radiomics. Given an anchor chest X-ray in a minibatch, the positive sample will be its radiomic feature view, and the negative samples will be other chest X-rays (either image or radiomics). Since the CLS could be regarded as the representation of all other tokens, we only need to maximize the agreement between them. Suppose \(I_{cls,k}\) and \(R_{cls,k}\) are the \(k-\)th image features and radiomic features in the minibatch, respectively, and \(sim()\) the cosine similarity, the loss function \(\mathcal{L}_{cl}\) is defined as \[\mathcal{L}_{cl}=-\log\frac{\exp(sim(g_{i}(I_{cls,k}),g_{r}(R_{cls,k}))/\tau) }{\sum_{k=1}^{N}\exp(sim(g_{i}(I_{cls,k}),g_{r}(R_{cls,k}))/\tau)} \tag{2}\]

Overall, we treat CheXT training as a semi-supervised multi-task learning. Since for chest x-rays, there exists two labels, disease class labels and pathology bounding box annotations. In our case, we only have access to the disease labels, however, bounding box annotations are more important than the disease class labels for chest x-rays. Here, when we say "semi-supervised", we refer to the bounding box annotations, not disease class labels. For multi-task, one task is supervised disease classification. The other is unsupervised cross-view contrastive learning.

The NIH dataset also includes high-quality bounding box annotations for 880 images by radiologists.

A significant difference between our method and existing baseline methods (Liu et al., 2019; Li et al., 2018) is that our method does not require any training data related to the bounding box while others use some percentage of these images for training.

We build our image branch encoder based on PS-ViT (Yue et al., 2021) and apply their default hyperparameters for training. The only difference is that we use a more shallow PS-ViT with 6 layers. For the radiomic branch encoder, since the radiomic features are already informative features, we use a more shallow Transformer (1-layers) to encode the radiomic features.

Abnormality classification is a multi-label classification problem. It assigns one or more labels among 8 cardiopulmonary diseases (Atelectasis, Cardiomegaly, Effusion, Infiltration, Mass, Nodule, Pneumonia, and Pneumothorax) to each input image at inference time. We conducted 3-fold cross-validation (Table 1). We compared CheXT with reference models, which have published state-of-the-art performance of disease classification on the NIH Chest X-ray dataset.

Compared to all baselines, CheXT achieves a mean AUC score of 0.839 using a dual-branch shallow Transformer across the 8 different diseases, which is 0.011 higher than the SOTA (uses DenseNet-121) (Rajpurkar et al., 2017) on disease classification. Specifically, our results significantly outperformed the best baseline models by 0.13, 0.09, and 0.02 in AUC for detecting Infiltration, Nodule, and Mass higher respectively. Besides, compared to the Transformer-based models, the key difference is that we utilize the extracted radiomic features for disease prediction, which improves the accuracy and adds the model's interpretability due to the utilization of self-designed handcrafted radiomic features.

During our experiments, we found that CheXT performs worse when very large bounding boxes are generated. This observation meets our expectations since radiomic features are very subtle to bounding boxes. Specifically, the extracted radiomic features are more robust if more focal and accurate bounding boxes are given.

In a calculated ratio, they help CheXT to learn both disease-level and patient-level discriminative visual features. The NIH Chest X-ray dataset has 880 images labeled by radiologists with the bounding box information. We have used this dataset to evaluate the performance of CheXT for abnormality localization.

For localization, we evaluated our detected regular rectangular regions against the annotated bounding boxes, using intersection over union ratio (IoU).

CheXT average performance across 8 diseases is significantly better than the baseline under all IoU thresholds. When the thresholds of IoU is set to 0.1, CheXT outperforms the baseline (Wang et al., 2017) in Cardiomegaly, infiltration, Mass, and Pneumonia. Even with higher thresholds, our model performs superior to baseline. For example, when evaluated at T(IoU) = 0.5, our "Cardiomegaly" accuracy is still 32%, while the reference model achieves only 18%.

More importantly, we also add the ViT as our additional baseline here, the results show that the radiomics branch and BYOA module can help the model learn more important regions, which also improves the model's interpretability.

In this paper, we propose an end-to-end knowledge-guided cross-attention Transformer, named CheXT that can jointly model abnormality classification and localization without the supervision of localization annotation of chest X-rays. Our approach differs from previous studies in the choice of universal modeling transformer, the use of radiomic features as prior knowledge, and a feedback loop for image and radiomic features to mutually interact with each other. Additionally, the project aims to mitigate current gaps in radiology by making prior knowledge more accessible to image data analytic and diagnostic assisting tools, with the hope that this will increase the model's interpretability. Experimental results demonstrate that our method outperforms the state-of-the-art algorithms, especially for the disease localization task, where our method can generate more accurate bounding boxes.

Input: Table 1: Comparison with the baseline models for AUC of each class and average AUC. For each column, bold values denote the best results.

Input: Figure 2: Overview of our proposed CheXT. It contains two branches, the Image branch and the Radiomics branch, to process the image and radiomic features (generated by the BYOA module shown in Figure 3), respectively. The output tokens are then fused by an efficient module via cross attention of the CLS tokens. Finally, the output of two CLS tokens (Icls andRcls) are used for disease classification. We minimize the classification errors via a focal loss. Since we train with both labeled and unlabeled images, we leverage a contrastive learning strategy. Specifically, we generate the image view zi = gi(Icls) by a projection head gi. Similarily, we generate the radiomic view zr = gr(Rcls) by another projection head gr. We maximize the agreement between zi and zr via a contrastive loss (NT-Xent). Of note, the contrastive loss is only active during the training.

Input: Figure 3: Overview of our BYOA module. For the input chest X-rays, we look at the self-attention of the CLS token of the Image branch on the heads of the final output of the cross attention module. Then we apply a threshold (0.1) (which means we only keep the top 10% active pixels in the whole attention map) to generate the bounding boxes. Then with the generated bounding boxes, we use the Pyradiomic tool as the radiomic extractor to extract the radiomic features.

Input: Figure 1: General overview of our CheXT framework for Cardiopulmonary Disease Classification and Localization from chest X-rays. CheXT takes the chest X-ray image as the input and outputs a heatmap for pathology localization, based on which the bounding box could be obtained. Radiomic features are further extracted from the bounded region and are fed to predict the disease.

Input: Figure 5: Examples of visualization of localization on the test images. The attention maps are generated from the self-attention maps of the CLS token. The ground-truth bounding boxes are shown in blue. The left image in each pair is the localization result of ViT (Dosovitskiy et al., 2020). The right one is our localization results. All examples are positive for corresponding disease labels. Best viewed in color.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the evidence and reviewer comments, here is my assessment:

The claim that "CheXT targets (semi-supervised) abnormality classification and localization from chest X-rays, enhanced by baked-in auxiliary knowledge guidance using radiomics" requires careful evaluation.

Key issues:
1. The "semi-supervised" characterization is questionable, as reviewers note all images have disease labels
2. While radiomics integration is demonstrated, performance actually drops for some disease classes after incorporating radiomics
3. The evidence shows solid methodology but lacks statistical significance tests and generalization studies
4. The localization capabilities are supported by comparative results, though limited to 880 annotated images

The claim moderately overstates the semi-supervised nature and unqualified benefits of radiomics integration, though the core technical approach is well-supported.

<score>3</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

CheXT targets (semi-supervised) abnormality classification and localization from chest X-rays, enhanced by baked-in auxiliary knowledge guidance using radiomics.

The review comment to be evaluated is:

1) Radiomics is a term that most people in the ICLR community are not familiar with. What is it exactily? Is it a set of radiological/image features or bio-markers in medical images? Or something else? The authors should consider explaining it in plain language or ML language.

2) Prior multi-modal feature fusion work in the chest x-ray domain should be discussed, examples below.

    - Moradi, Mehdi, Ali Madani, Yaniv Gur, Yufan Guo, and Tanveer Syeda-Mahmood. "Bimodal network architectures for automatic generation of image annotation from text." arXiv preprint arXiv:1809.01610 (2018).

    - Wang, Xiaosong, Yifan Peng, Le Lu, Zhiyong Lu, and Ronald M. Summers. "TieNet: Text-Image Embedding Network for Common Thorax Disease Classification and Reporting in Chest X-rays." arXiv e-prints (2018): arXiv-1801.

    - Xue, Yuan, and Xiaolei Huang. "Improved disease classification in chest x-rays with transferred features from report generation." In International Conference on Information Processing in Medical Imaging, pp. 125-138. Springer, Cham, 2019.

    - Liao, Ruizhi, Daniel Moyer, Miriam Cha, Keegan Quigley, Seth Berkowitz, Steven Horng, Polina Golland, and William M. Wells. "Multimodal Representation Learning via Maximization of Local Mutual Information." arXiv preprint arXiv:2103.04537 (2021).

3) I don't see "prior knowledge" and how it's utilized in this work. Isn't the so called prior knowledge in this paper essentially extracted from the data (chest x-ray images)?

4) What are the patch sizes? How are they determined?

5) There are 14 pathology labels in the NIH chest x-ray dataset. Why/how did the authors select the 8 classes out of the 14?

6) In Table 1, make the best results bold instead of in red.

7) Are the AUC values generated on the test set? If so, I'm concerned about overfitting by over tuning the hyper-parameters.

The evidence to be evaluated is:The design of radiomics involves biological and medical data and prior knowledge. Thus, radiomics vastly enriches images and expands the horizons of the image toward in-vivo biologic information extraction (Gillies et al., 2016). In image-based biomarkers for cancer staging and prognostication, radiomics had shown promising power (Nasief et al., 2019). Radiomics extracts quantitative data from medical images to represent tumor phenotypes, such as spatial heterogeneity of a tumor and spatial response variations.

(Huang et al., 2018) showed that radiomics could increase the positive predictive value and reduce the false-positive rate in lung cancer screening for small nodules compared with radiologists.

We believe that the joint utilization of radiomics and interpretable learning techniques in our framework can further advance accurate yet interpretable learning in the medical image domain.

(Han et al., 2021) applied pre-extracted radiomic features to guide pneumonia detection from chest X-ray images.

An overview of CheXT is illustrated in Figure 2. In the following subsections, we will first present Cross-attention Vision Transformer (CrossViT), a recent two-branch ViT backbone on which CheXT is built, and then describe our many unique improvements customized for Chest X-ray analysis.

In CheXT, we extend their idea by treating image itself as one "view" and the radiomic feature extracted from the same image as another "view" (Figure 2). The two views are the jointly learned by interacting through cross-attention. Transformer serves as the modality-agnostic backbone for both. Specifically, we introduce a dual-branch cross-attention Transformer where the first (primary) branch operates the image part, while the second (complementary) branch handles the radiomic features. To resolve the "chicken-and-egg" dilemma in extracting reliable radiomic features without bounding boxes, we have designed a novel _Bootstrap Your Own Attention_ (**BYOA**) module, using feedbacks to learn region localization for extracting radiomic features.

The primary image branch uses a Progress-Sampling ViT (PS-ViT) (Yue et al., 2021) as its backbone. Unlike the vanilla ViT that splits images into fixed-size tokens, PS-ViT utilizes an iterative and progressive sampling strategy to locate discriminative regions and avoid over-partition object structures. We experimentally observed PS-ViT outperforms ViT and other variants in our framework because it generates higher-quality and more structure-aware attention maps, which are crucial for estimating the pathology localization during training. **Radiomics Branch.** The complementary radiomics branch is for processing radiomic features. Handcrafted features usually cover a wide range of categories, such as first-order (basic intensity and shaped-based features), second-order (texture features extracted from various matrices), and more advanced features including those calculated from Fourier and wavelet transforms. Specifically, the radiomic features are composed of the following categories:* First-Order statistics features to measure the distribution of voxel intensities within the bounding boxes. The features include energy (the measurement of the magnitude of voxel values), entropy (the measurement of uncertainty in the image values), and max/mean/median gray level intensity within the region of interest.

* Shape-based features, such as Mesh Surface, Pixel Surface, and Perimeter.

* Gray-level features, such as Gray Level Co-occurrence Matrix (GLCM), Gray Level Size Zone (GLSZM), Gray Level Run Length Matrix (GLRLM), Neighboring Gray Tone Difference Matrix (NGTDM), and Gray Level Dependence Matrix (GLDM) features. In short, radiomic features are a set of quantitative features that can describe the characteristics of medical images. In our framework, we aim to make the hidden features of the CLS similar to the radiomic features to learn the localization of pathologies in the chest X-rays. For this branch, we use the vanilla Transformer (Liu et al., 2017) as the radiomic features encoder.

Our main _roadblock_ is how to generate robust radiomic features without pathology localization. On one hand, radiomic features are dependent on and highly sensitive to the local image regions of interest, for which we have no bounding box annotation. On the other hand, image features would benefit from the guidance from radiomic features that encode important domain knowledge. The learning of image and radiomic features are fully entangled, forming a challenging "chicken-and-egg" loop. To address this issue, we design **BYOA** to constitute an end-to-end feedback loop that can bootstrap accurate pathology localization from image data, without any bounding box used for training (Figure 3). BYOA contains two components: attention map generation and radiomic feature extraction.

Then we apply a threshold on the self-attention maps to generate bounding boxes for the extraction of radiomic features. The choice of the threshold will influence the quality of the radiomic features. Specifically, the threshold is designed as how much active pixels to keep in the generated attention map. The more active pixels we keep, the larger the generated bounding box is.

Given the original images and generated bounding boxes, we used the Pyradiomic tool to extract radiomic features (Van Griethuysen et al., 2017).

As shown in Figure 2, CheXT is trained using the linear combination of the supervised focal classification and unsupervised contrastive losses. For the supervised classification, considering that the chest X-ray dataset is usually highly imbalanced, we adopt the focal loss (Pasupa et al., 2020). For unsupervised contrastive learning, we use the cross-view contrastive loss (Chen et al., 2020).

We feed the output of the CLS tokens of two branches \(I_{cls}\) and \(R_{cls}\) to a simple linear classifier.

Our contrastive loss extends the normalized temperature scaled cross-entropy loss (NT-Xent). The difference is that we maximize agreement between two feature views extracted from different input formats, one the image and the other radiomics. Given an anchor chest X-ray in a minibatch, the positive sample will be its radiomic feature view, and the negative samples will be other chest X-rays (either image or radiomics). Since the CLS could be regarded as the representation of all other tokens, we only need to maximize the agreement between them. Suppose \(I_{cls,k}\) and \(R_{cls,k}\) are the \(k-\)th image features and radiomic features in the minibatch, respectively, and \(sim()\) the cosine similarity, the loss function \(\mathcal{L}_{cl}\) is defined as \[\mathcal{L}_{cl}=-\log\frac{\exp(sim(g_{i}(I_{cls,k}),g_{r}(R_{cls,k}))/\tau) }{\sum_{k=1}^{N}\exp(sim(g_{i}(I_{cls,k}),g_{r}(R_{cls,k}))/\tau)} \tag{2}\]

Overall, we treat CheXT training as a semi-supervised multi-task learning. Since for chest x-rays, there exists two labels, disease class labels and pathology bounding box annotations. In our case, we only have access to the disease labels, however, bounding box annotations are more important than the disease class labels for chest x-rays. Here, when we say "semi-supervised", we refer to the bounding box annotations, not disease class labels. For multi-task, one task is supervised disease classification. The other is unsupervised cross-view contrastive learning.

The NIH dataset also includes high-quality bounding box annotations for 880 images by radiologists.

A significant difference between our method and existing baseline methods (Liu et al., 2019; Li et al., 2018) is that our method does not require any training data related to the bounding box while others use some percentage of these images for training.

We build our image branch encoder based on PS-ViT (Yue et al., 2021) and apply their default hyperparameters for training. The only difference is that we use a more shallow PS-ViT with 6 layers. For the radiomic branch encoder, since the radiomic features are already informative features, we use a more shallow Transformer (1-layers) to encode the radiomic features.

Abnormality classification is a multi-label classification problem. It assigns one or more labels among 8 cardiopulmonary diseases (Atelectasis, Cardiomegaly, Effusion, Infiltration, Mass, Nodule, Pneumonia, and Pneumothorax) to each input image at inference time. We conducted 3-fold cross-validation (Table 1). We compared CheXT with reference models, which have published state-of-the-art performance of disease classification on the NIH Chest X-ray dataset.

Compared to all baselines, CheXT achieves a mean AUC score of 0.839 using a dual-branch shallow Transformer across the 8 different diseases, which is 0.011 higher than the SOTA (uses DenseNet-121) (Rajpurkar et al., 2017) on disease classification. Specifically, our results significantly outperformed the best baseline models by 0.13, 0.09, and 0.02 in AUC for detecting Infiltration, Nodule, and Mass higher respectively. Besides, compared to the Transformer-based models, the key difference is that we utilize the extracted radiomic features for disease prediction, which improves the accuracy and adds the model's interpretability due to the utilization of self-designed handcrafted radiomic features.

During our experiments, we found that CheXT performs worse when very large bounding boxes are generated. This observation meets our expectations since radiomic features are very subtle to bounding boxes. Specifically, the extracted radiomic features are more robust if more focal and accurate bounding boxes are given.

In a calculated ratio, they help CheXT to learn both disease-level and patient-level discriminative visual features. The NIH Chest X-ray dataset has 880 images labeled by radiologists with the bounding box information. We have used this dataset to evaluate the performance of CheXT for abnormality localization.

For localization, we evaluated our detected regular rectangular regions against the annotated bounding boxes, using intersection over union ratio (IoU).

CheXT average performance across 8 diseases is significantly better than the baseline under all IoU thresholds. When the thresholds of IoU is set to 0.1, CheXT outperforms the baseline (Wang et al., 2017) in Cardiomegaly, infiltration, Mass, and Pneumonia. Even with higher thresholds, our model performs superior to baseline. For example, when evaluated at T(IoU) = 0.5, our "Cardiomegaly" accuracy is still 32%, while the reference model achieves only 18%.

More importantly, we also add the ViT as our additional baseline here, the results show that the radiomics branch and BYOA module can help the model learn more important regions, which also improves the model's interpretability.

In this paper, we propose an end-to-end knowledge-guided cross-attention Transformer, named CheXT that can jointly model abnormality classification and localization without the supervision of localization annotation of chest X-rays. Our approach differs from previous studies in the choice of universal modeling transformer, the use of radiomic features as prior knowledge, and a feedback loop for image and radiomic features to mutually interact with each other. Additionally, the project aims to mitigate current gaps in radiology by making prior knowledge more accessible to image data analytic and diagnostic assisting tools, with the hope that this will increase the model's interpretability. Experimental results demonstrate that our method outperforms the state-of-the-art algorithms, especially for the disease localization task, where our method can generate more accurate bounding boxes.

Input: Table 1: Comparison with the baseline models for AUC of each class and average AUC. For each column, bold values denote the best results.

Input: Figure 2: Overview of our proposed CheXT. It contains two branches, the Image branch and the Radiomics branch, to process the image and radiomic features (generated by the BYOA module shown in Figure 3), respectively. The output tokens are then fused by an efficient module via cross attention of the CLS tokens. Finally, the output of two CLS tokens (Icls andRcls) are used for disease classification. We minimize the classification errors via a focal loss. Since we train with both labeled and unlabeled images, we leverage a contrastive learning strategy. Specifically, we generate the image view zi = gi(Icls) by a projection head gi. Similarily, we generate the radiomic view zr = gr(Rcls) by another projection head gr. We maximize the agreement between zi and zr via a contrastive loss (NT-Xent). Of note, the contrastive loss is only active during the training.

Input: Figure 3: Overview of our BYOA module. For the input chest X-rays, we look at the self-attention of the CLS token of the Image branch on the heads of the final output of the cross attention module. Then we apply a threshold (0.1) (which means we only keep the top 10% active pixels in the whole attention map) to generate the bounding boxes. Then with the generated bounding boxes, we use the Pyradiomic tool as the radiomic extractor to extract the radiomic features.

Input: Figure 1: General overview of our CheXT framework for Cardiopulmonary Disease Classification and Localization from chest X-rays. CheXT takes the chest X-ray image as the input and outputs a heatmap for pathology localization, based on which the bounding box could be obtained. Radiomic features are further extracted from the bounded region and are fed to predict the disease.

Input: Figure 5: Examples of visualization of localization on the test images. The attention maps are generated from the self-attention maps of the CLS token. The ground-truth bounding boxes are shown in blue. The left image in each pair is the localization result of ViT (Dosovitskiy et al., 2020). The right one is our localization results. All examples are positive for corresponding disease labels. Best viewed in color.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After careful review, the claim "CheXT targets (semi-supervised) abnormality classification and localization from chest X-rays, enhanced by baked-in auxiliary knowledge guidance using radiomics" appears to be well-stated based on the evidence provided. The paper clearly demonstrates:

1. Semi-supervised learning approach (uses disease labels but not bounding box annotations)
2. Both classification and localization capabilities (shown in results tables and visualizations)
3. Integration of radiomics as auxiliary knowledge (detailed explanation of radiomic features and their incorporation)
4. Experimental validation showing improvements over baselines

The claim accurately reflects the scope and capabilities without overstating results. The relevant reviewer comments (#1 and #3) question terminology clarity but don't invalidate the fundamental claim.

<score>1</score>

Justification:
- Evidence clearly shows radiomics integration through BYOA module and cross-attention architecture (Figures 2-3)
- Results demonstrate improved performance in both classification (Table 1) and localization tasks
- Semi-supervised nature is explicitly discussed regarding bounding box annotations
- Authors acknowledge limitations and carefully scope their claims
- Implementation details and methodology are thoroughly documented
- Claims are supported by quantitative results and comparisons


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

CheXT targets (semi-supervised) abnormality classification and localization from chest X-rays, enhanced by baked-in auxiliary knowledge guidance using radiomics.

The review comment to be evaluated is:

Strength:
- The proposed approach and considering radiomic features along with learnt features is valuable and very relevant to the community.

Main Concerns and comments to improve the paper:
- The paper does not consider evaluation of the method on more publicly available datasets and the study is only limited to NIH-14 Chest X-ray data.
- Baselines and state of the art results on the test data: One important baseline is to make the prediction using only radiomic features.  Also,  [Han’20]  has reported much higher performance on NIH-14 and for the same task which they have not been included in the paper.
Improvement over the CNN baseline on classification: We see 0.011 improvement in mean AUC over Rajpurkar’17 as one of the baseline which is not significant.
- Details of the comparison setups are missing. I am wondering if these numbers are collected from the reported values by authors or they ran every SOTA against their data division (less likely since there is no error bound reported for SOTAs). And if they are-implemented or re-evaluated each method what are the details and consideration for a fair comparison and reproducing the results.
- Main contribution of the paper is to activate usage or radiomic features within the blackbox  structure of deep networks, however, there is no discussion or insight around the meaning and importance of multiple radiomic features that have been used. There are also works that include prior knowledge for Chest X-ray classification including [Han’20]. These features either unlock a better performance (which seems not being really successful in) or demystify the learnt feature space in a way that is pleasant for human interpreters. The paper does not provide such results.

Suggestion to improve the work:
- It would be useful to add some more explanation around radiomics term, feature extraction procedure and meaning of it, since this is not a common term for the machine learning community.
- Consider boosting the results and observation using a few more test data such as MIMIC and CheXpert that have the similar label space as NIH-14 and publicly being used in most of the related papers.
- The above mentioned datasets also can be used for evaluation of the out-of-domain generalization of the method which has not been discussed in the paper.
- Experimental setup and evaluation toward the SOTA can improve significantly. For the comparison purpose I would consider producing a statistical error range with either k-fold cross validation or boot-strapping for both CheXT and SOTAs. I would personally see a proper statistical comparison between a single best SOTA with the proposed method more valuable than having multiple models without any statistical validation.
- Adding interpretation around radiomic feature meaning, how the proposed method unlocked one or more of the 3 listed limitations of CNN methods that has been discussed in the Introduction.
- It is worth also discussing failure cases and the reasoning behind lower performance for some of the conditions such as effusion and pneumonia.

[Han'20] Han, Yan, Chongyan Chen, Liyan Tang, Mingquan Lin, Ajay Jaiswal, Song Wang, Ahmed Tewfik, George Shih, Ying Ding, and Yifan Peng. "Using Radiomics as Prior Knowledge for Thorax Disease Classification and Localization in Chest X-rays." arXiv preprint arXiv:2011.12506 (2020).

The evidence to be evaluated is:The design of radiomics involves biological and medical data and prior knowledge. Thus, radiomics vastly enriches images and expands the horizons of the image toward in-vivo biologic information extraction (Gillies et al., 2016). In image-based biomarkers for cancer staging and prognostication, radiomics had shown promising power (Nasief et al., 2019). Radiomics extracts quantitative data from medical images to represent tumor phenotypes, such as spatial heterogeneity of a tumor and spatial response variations.

(Huang et al., 2018) showed that radiomics could increase the positive predictive value and reduce the false-positive rate in lung cancer screening for small nodules compared with radiologists.

We believe that the joint utilization of radiomics and interpretable learning techniques in our framework can further advance accurate yet interpretable learning in the medical image domain.

(Han et al., 2021) applied pre-extracted radiomic features to guide pneumonia detection from chest X-ray images.

An overview of CheXT is illustrated in Figure 2. In the following subsections, we will first present Cross-attention Vision Transformer (CrossViT), a recent two-branch ViT backbone on which CheXT is built, and then describe our many unique improvements customized for Chest X-ray analysis.

In CheXT, we extend their idea by treating image itself as one "view" and the radiomic feature extracted from the same image as another "view" (Figure 2). The two views are the jointly learned by interacting through cross-attention. Transformer serves as the modality-agnostic backbone for both. Specifically, we introduce a dual-branch cross-attention Transformer where the first (primary) branch operates the image part, while the second (complementary) branch handles the radiomic features. To resolve the "chicken-and-egg" dilemma in extracting reliable radiomic features without bounding boxes, we have designed a novel _Bootstrap Your Own Attention_ (**BYOA**) module, using feedbacks to learn region localization for extracting radiomic features.

The primary image branch uses a Progress-Sampling ViT (PS-ViT) (Yue et al., 2021) as its backbone. Unlike the vanilla ViT that splits images into fixed-size tokens, PS-ViT utilizes an iterative and progressive sampling strategy to locate discriminative regions and avoid over-partition object structures. We experimentally observed PS-ViT outperforms ViT and other variants in our framework because it generates higher-quality and more structure-aware attention maps, which are crucial for estimating the pathology localization during training. **Radiomics Branch.** The complementary radiomics branch is for processing radiomic features. Handcrafted features usually cover a wide range of categories, such as first-order (basic intensity and shaped-based features), second-order (texture features extracted from various matrices), and more advanced features including those calculated from Fourier and wavelet transforms. Specifically, the radiomic features are composed of the following categories:* First-Order statistics features to measure the distribution of voxel intensities within the bounding boxes. The features include energy (the measurement of the magnitude of voxel values), entropy (the measurement of uncertainty in the image values), and max/mean/median gray level intensity within the region of interest.

* Shape-based features, such as Mesh Surface, Pixel Surface, and Perimeter.

* Gray-level features, such as Gray Level Co-occurrence Matrix (GLCM), Gray Level Size Zone (GLSZM), Gray Level Run Length Matrix (GLRLM), Neighboring Gray Tone Difference Matrix (NGTDM), and Gray Level Dependence Matrix (GLDM) features. In short, radiomic features are a set of quantitative features that can describe the characteristics of medical images. In our framework, we aim to make the hidden features of the CLS similar to the radiomic features to learn the localization of pathologies in the chest X-rays. For this branch, we use the vanilla Transformer (Liu et al., 2017) as the radiomic features encoder.

Our main _roadblock_ is how to generate robust radiomic features without pathology localization. On one hand, radiomic features are dependent on and highly sensitive to the local image regions of interest, for which we have no bounding box annotation. On the other hand, image features would benefit from the guidance from radiomic features that encode important domain knowledge. The learning of image and radiomic features are fully entangled, forming a challenging "chicken-and-egg" loop. To address this issue, we design **BYOA** to constitute an end-to-end feedback loop that can bootstrap accurate pathology localization from image data, without any bounding box used for training (Figure 3). BYOA contains two components: attention map generation and radiomic feature extraction.

Then we apply a threshold on the self-attention maps to generate bounding boxes for the extraction of radiomic features. The choice of the threshold will influence the quality of the radiomic features. Specifically, the threshold is designed as how much active pixels to keep in the generated attention map. The more active pixels we keep, the larger the generated bounding box is.

Given the original images and generated bounding boxes, we used the Pyradiomic tool to extract radiomic features (Van Griethuysen et al., 2017).

As shown in Figure 2, CheXT is trained using the linear combination of the supervised focal classification and unsupervised contrastive losses. For the supervised classification, considering that the chest X-ray dataset is usually highly imbalanced, we adopt the focal loss (Pasupa et al., 2020). For unsupervised contrastive learning, we use the cross-view contrastive loss (Chen et al., 2020).

We feed the output of the CLS tokens of two branches \(I_{cls}\) and \(R_{cls}\) to a simple linear classifier.

Our contrastive loss extends the normalized temperature scaled cross-entropy loss (NT-Xent). The difference is that we maximize agreement between two feature views extracted from different input formats, one the image and the other radiomics. Given an anchor chest X-ray in a minibatch, the positive sample will be its radiomic feature view, and the negative samples will be other chest X-rays (either image or radiomics). Since the CLS could be regarded as the representation of all other tokens, we only need to maximize the agreement between them. Suppose \(I_{cls,k}\) and \(R_{cls,k}\) are the \(k-\)th image features and radiomic features in the minibatch, respectively, and \(sim()\) the cosine similarity, the loss function \(\mathcal{L}_{cl}\) is defined as \[\mathcal{L}_{cl}=-\log\frac{\exp(sim(g_{i}(I_{cls,k}),g_{r}(R_{cls,k}))/\tau) }{\sum_{k=1}^{N}\exp(sim(g_{i}(I_{cls,k}),g_{r}(R_{cls,k}))/\tau)} \tag{2}\]

Overall, we treat CheXT training as a semi-supervised multi-task learning. Since for chest x-rays, there exists two labels, disease class labels and pathology bounding box annotations. In our case, we only have access to the disease labels, however, bounding box annotations are more important than the disease class labels for chest x-rays. Here, when we say "semi-supervised", we refer to the bounding box annotations, not disease class labels. For multi-task, one task is supervised disease classification. The other is unsupervised cross-view contrastive learning.

The NIH dataset also includes high-quality bounding box annotations for 880 images by radiologists.

A significant difference between our method and existing baseline methods (Liu et al., 2019; Li et al., 2018) is that our method does not require any training data related to the bounding box while others use some percentage of these images for training.

We build our image branch encoder based on PS-ViT (Yue et al., 2021) and apply their default hyperparameters for training. The only difference is that we use a more shallow PS-ViT with 6 layers. For the radiomic branch encoder, since the radiomic features are already informative features, we use a more shallow Transformer (1-layers) to encode the radiomic features.

Abnormality classification is a multi-label classification problem. It assigns one or more labels among 8 cardiopulmonary diseases (Atelectasis, Cardiomegaly, Effusion, Infiltration, Mass, Nodule, Pneumonia, and Pneumothorax) to each input image at inference time. We conducted 3-fold cross-validation (Table 1). We compared CheXT with reference models, which have published state-of-the-art performance of disease classification on the NIH Chest X-ray dataset.

Compared to all baselines, CheXT achieves a mean AUC score of 0.839 using a dual-branch shallow Transformer across the 8 different diseases, which is 0.011 higher than the SOTA (uses DenseNet-121) (Rajpurkar et al., 2017) on disease classification. Specifically, our results significantly outperformed the best baseline models by 0.13, 0.09, and 0.02 in AUC for detecting Infiltration, Nodule, and Mass higher respectively. Besides, compared to the Transformer-based models, the key difference is that we utilize the extracted radiomic features for disease prediction, which improves the accuracy and adds the model's interpretability due to the utilization of self-designed handcrafted radiomic features.

During our experiments, we found that CheXT performs worse when very large bounding boxes are generated. This observation meets our expectations since radiomic features are very subtle to bounding boxes. Specifically, the extracted radiomic features are more robust if more focal and accurate bounding boxes are given.

In a calculated ratio, they help CheXT to learn both disease-level and patient-level discriminative visual features. The NIH Chest X-ray dataset has 880 images labeled by radiologists with the bounding box information. We have used this dataset to evaluate the performance of CheXT for abnormality localization.

For localization, we evaluated our detected regular rectangular regions against the annotated bounding boxes, using intersection over union ratio (IoU).

CheXT average performance across 8 diseases is significantly better than the baseline under all IoU thresholds. When the thresholds of IoU is set to 0.1, CheXT outperforms the baseline (Wang et al., 2017) in Cardiomegaly, infiltration, Mass, and Pneumonia. Even with higher thresholds, our model performs superior to baseline. For example, when evaluated at T(IoU) = 0.5, our "Cardiomegaly" accuracy is still 32%, while the reference model achieves only 18%.

More importantly, we also add the ViT as our additional baseline here, the results show that the radiomics branch and BYOA module can help the model learn more important regions, which also improves the model's interpretability.

In this paper, we propose an end-to-end knowledge-guided cross-attention Transformer, named CheXT that can jointly model abnormality classification and localization without the supervision of localization annotation of chest X-rays. Our approach differs from previous studies in the choice of universal modeling transformer, the use of radiomic features as prior knowledge, and a feedback loop for image and radiomic features to mutually interact with each other. Additionally, the project aims to mitigate current gaps in radiology by making prior knowledge more accessible to image data analytic and diagnostic assisting tools, with the hope that this will increase the model's interpretability. Experimental results demonstrate that our method outperforms the state-of-the-art algorithms, especially for the disease localization task, where our method can generate more accurate bounding boxes.

Input: Table 1: Comparison with the baseline models for AUC of each class and average AUC. For each column, bold values denote the best results.

Input: Figure 2: Overview of our proposed CheXT. It contains two branches, the Image branch and the Radiomics branch, to process the image and radiomic features (generated by the BYOA module shown in Figure 3), respectively. The output tokens are then fused by an efficient module via cross attention of the CLS tokens. Finally, the output of two CLS tokens (Icls andRcls) are used for disease classification. We minimize the classification errors via a focal loss. Since we train with both labeled and unlabeled images, we leverage a contrastive learning strategy. Specifically, we generate the image view zi = gi(Icls) by a projection head gi. Similarily, we generate the radiomic view zr = gr(Rcls) by another projection head gr. We maximize the agreement between zi and zr via a contrastive loss (NT-Xent). Of note, the contrastive loss is only active during the training.

Input: Figure 3: Overview of our BYOA module. For the input chest X-rays, we look at the self-attention of the CLS token of the Image branch on the heads of the final output of the cross attention module. Then we apply a threshold (0.1) (which means we only keep the top 10% active pixels in the whole attention map) to generate the bounding boxes. Then with the generated bounding boxes, we use the Pyradiomic tool as the radiomic extractor to extract the radiomic features.

Input: Figure 1: General overview of our CheXT framework for Cardiopulmonary Disease Classification and Localization from chest X-rays. CheXT takes the chest X-ray image as the input and outputs a heatmap for pathology localization, based on which the bounding box could be obtained. Radiomic features are further extracted from the bounded region and are fed to predict the disease.

Input: Figure 5: Examples of visualization of localization on the test images. The attention maps are generated from the self-attention maps of the CLS token. The ground-truth bounding boxes are shown in blue. The left image in each pair is the localization result of ViT (Dosovitskiy et al., 2020). The right one is our localization results. All examples are positive for corresponding disease labels. Best viewed in color.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the evidence and reviewer comments, I assess the main claim about CheXT's capabilities and framework.

Summary: While CheXT does implement a semi-supervised approach combining radiomics with deep learning for chest X-ray analysis, there are several limitations that make the claim somewhat overstated:

1. Limited validation on only NIH-14 dataset (noted in review)
2. Marginal improvement over baselines (0.011 AUC increase)
3. Missing statistical validation and error bounds for comparisons
4. Limited discussion of radiomic features' interpretability benefits
5. However, the core technical approach is well-documented with clear methodology and shows some improvements in localization tasks

The claim could be more precise by acknowledging these limitations and specifying the modest performance gains rather than implying broad enhancement through radiomics integration.

<score>3</score>