# Pathologist-Like Explanations Unveiled: an Explainable Deep Learning System
for White Blood Cell Classification

Aditya Shankar Pal  Debojyoti Biswas  Joy Mahapatra  Debasis Banerjee  Prantar
Chakrabarti and Utpal Garain  Member, IEEE Aditya Shankar Pal, Debojyoti
Biswas, Joy Mahapatra, and Utpal Garain are with the Indian Statistical
Institute, Kolkata 700108, India (e-mail<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>utpal@isical.ac.in).
Debasis Bandyopadhyay is with the Drs Tribedi & Roy Diagnostic Laboratory,
Kolkata, India (e-mail: debasisbanerjee.haematology@gmail.com)Prantar
Chakrabarti is with the Zoho Corporation, India and Centre for Digital
Healthcare Technologies, MAKAUT, India (e-mail:
prantar@gmail.com)_Corresponding author: Aditya Shankar Pal_

###### Abstract

White blood cells (WBCs) play a crucial role in safeguarding the human body
against pathogens and foreign substances. Leveraging the abundance of WBC
imaging data and the power of deep learning algorithms, automated WBC analysis
has the potential for remarkable accuracy. However, the capability of deep
learning models to explain their WBC classification remains largely
unexplored. In this study, we introduce _HemaX_ , an explainable deep neural
network-based model that produces pathologist-like explanations using five
attributes: granularity, cytoplasm color, nucleus shape, size relative to red
blood cells, and nucleus to cytoplasm ratio (N:C), along with cell
classification, localization, and segmentation. HemaX is trained and evaluated
on a novel dataset, LeukoX, comprising 467 blood smear images encompassing ten
(10) WBC types. The proposed model achieves impressive results, with an
average classification accuracy of 81.08% and a Jaccard index of 89.16% for
cell localization. Additionally, HemaX performs well in generating the five
explanations with a normalized mean square error of 0.0317 for N:C ratio and
over 80% accuracy for the other four attributes. Comprehensive experiments
comparing against multiple state-of-the-art models demonstrate that HemaX’s
classification accuracy remains unaffected by its ability to provide
explanations. Moreover, empirical analyses and validation by expert
hematologists confirm the faithfulness of explanations predicted by our
proposed model.

This study addresses the issue of explainability in white blood cell
classification. The robust performance of HemaX in WBC classification, along
with its ability to provide explanations, is expected to enhance pathologists’
confidence in the reliability of this system. This study differs from previous
research, which primarily evaluated Artificial Intelligence (AI) systems for
WBC classification while often keeping their inner workings opaque. HemaX’s
key advantage lies in its unified approach, enabling it to seamlessly perform
multiple tasks without requiring fundamental architectural changes. This sets
HemaX apart from previous systems and underscores its versatility. The
creation of the LeukoX dataset introduces significant enhancements to existing
WBC datasets, which previously focused solely on classification and
segmentation challenges. This new dataset for the explainable WBC
classification task promotes innovation and advancement in the use of deep
learning models for both medical and technological purposes.

Medical Image Analysis, Hematology, WBC classification, Deep Neural Models,
Explainable AI (XAI)

##  1 Introduction

White blood cells (WBCs), also known as leukocytes, are a diverse group of
nucleated blood cells responsible for defending the body against pathogens and
foreign substances. The normal range of leukocytes in the blood typically
falls between 4,000 to 10,000 per microliter [1]. Deviations from these levels
can indicate various illnesses such as lymphoma, leukemia, and viral
infections, guiding treatment decisions. Therefore, the analysis of leukocytes
is crucial for maintaining and diagnosing health conditions.

Conventional blood smear analysis requires pathologists to meticulously
examine each region under a microscope, making it a laborious and time-
consuming task prone to human errors [2]. Machine learning-based approaches
for leukocyte classification have emerged to address these challenges [3, 4,
5]. These traditional machine learning-based techniques typically involve
several stages: pre-processing, segmentation, feature extraction, and
classification [6, 7, 8, 9]. This framework suffers from two major drawbacks:
(i) human-engineered feature extraction method and (ii) any errors in the
early modules badly affect the final classification accuracy.

In recent years, deep learning models have made manual feature extraction
obsolete and demonstrated promising results in medical image analysis, showing
improved accuracy and robustness [10, 11, 12]. Numerous deep learning-based
models have also been developed for white blood cell analysis [13, 14, 15, 16,
17, 18, 19, 20, 21]. However, a notable limitation of deep learning models is
their inability to offer human-interpretable explanations for their
predictions [22, 23, 24, 25]. Consequently, clinicians may lack confidence in
a diagnostic system that cannot provide explanations when required [26].

In this study, we propose an explainable end-to-end model, called HemaX, to
bridge the gap between the deep learning paradigm and medical professionals by
providing them with insights into the model’s decision-making process. Unlike
existing models, HemaX can simultaneously produce pathologist-like
explanations for five attributes, along with cell classification and
localization. This unique feature sets HemaX apart and shows its potential to
improve the accuracy and efficiency of blood cell categorization, thus
assisting medical practitioners in making more precise diagnoses. Figure 1
illustrates the capabilities of HemaX upon receiving an input image. It
efficiently locates the cell, provides segmentation with cytoplasm and nucleus
masks, and generates a comprehensive report, offering cell type information
and values for the five attributes commonly used by pathologists to classify
WBCs.

Figure 1: Objectives achieved by HemaX

The proposed model is trained and evaluated on a newly developed explainable
dataset focused on leukocytes. While many publicly available datasets on WBC
imaging data address classification and segmentation, they often lack in
appropriate annotations for tasks like generating/ predicting explanations. To
address this gap, we introduce LeukoX, a novel dataset created in
collaboration with a renowned diagnostic laboratory (Section 2). Experiments
on the LeukoX dataset demonstrate that HemaX achieves a classification
accuracy, precision, and $F_{1}$ score of $0.8108,0.8351,$ and $0.8089$,
respectively, outperforming other competing classifiers such as Faster RCNN
[27], CUSS-Net [16], and Tavakoli’s algorithm [7] significantly. We have also
shown the generalizability of LeukoX on two publicly available datasets namely
LISC [2] and Raabin [28]. In brief, the significant contributions presented in
this paper are as follows:

  1. 1.

We propose a transformer encoder-decoder based model, HemaX, for
simultaneously classifying, segmenting and explaining the cell types of white
blood cells.

  2. 2.

We compiled the colored images of peripheral blood smear from 10 cell types
into a new dataset, LeukoX. The LeukoX dataset contains information regarding
the cell type, bounding box location, segmentation mask and pathologist-like
explanations for five attributes associated with each cell present in it.

  3. 3.

Experiments conducted on LeukoX, LISC and Raabin dataset show that HemaX
performs above par compared to the other state-of-the-art classifiers.

  4. 4.

Empirical analysis and expert evaluation further validate the results and
establish the faithfulness of HemaX.

The remaining portion of the paper is structured as follows. The development
of the LeukoX dataset is outlined in Section 2. A comprehensive description of
the proposed HemaX model, along with a technique for establishing
faithfulness, is provided in Section 3. Subsequently, Section 4 offers
insights into the baseline implementation, the process of selecting the best
HemaX model, and its comparison across the LeukoX, LISC, and Raabin datasets
with various state-of-the-art models. Additionally, the section also engages
in a discussion of the results, and establishes the faithfulness of the
explanations predicted by the proposed model as well as their verification by
an expert hematologist. Finally, the paper is concluded in Section 5.

##  2 LeukoX Dataset

We have developed LeukoX, a dataset containing peripheral blood smear (PBS)
images in collaboration with Drs. Roy and Tribedi Diagnostic Laboratory
Kolkata111https://www.tribediandroy.com/. The dataset is prepared under
ethical clearance from the Ethics Committee, Fortis
Hospital222https://www.fortishealthcare.com/india/fortis-hospital-in-
anandapur-kolkata-west-bengal. The dataset comprises peripheral blood smear
(PBS) images stained with the Leishman Giemsa procedure, acquired at $100x$
magnification using the wide-angle camera with a 12-megapixel f/1.8 aperture
on the iPhone XR. LeukoX contains cells from ten distinct WBC types:
neutrophils, lymphocytes, eosinophils, monocytes, basophils, band cells,
metamyelocytes, myelocytes, promyelocytes, and blast cells. The images come in
two resolutions: $4032\times 3024$ and $1024\times 768$. The dataset includes
50 PBS images for each of the nine WBC types and 17 PBS images for blast
cells. The cells are manually annotated by an expert hematologist, and
segmentation masks for the nucleus and cytoplasm regions are generated using
the Computer Vision Annotation Tool (CVAT)333https://github.com/opencv/cvat.
Additionally, each sample is annotated with five attributes characterizing the
cell type: granularity (‘yes’ or ‘no’), cytoplasm color (‘eosinophilic’ or
‘basophilic’), nucleus shape (‘horseshoe-shaped/kidney bean’,
‘bilobed/multilobed’, or ‘round/oval’), size with respect to red blood cells
(RBCs) (‘larger’, ‘nearly similar’, or ‘smaller’), and the nucleus to
cytoplasm ratio (N:C ratio). The N:C ratio is determined by calculating the
number of pixels in the nucleus region to the total number of pixels in the
cytoplasm region.

The construction of LeukoX dataset introduces several noteworthy strengths.
First, it capitalizes on the capabilities of smartphone cameras to capture WBC
images, aligning with the pursuit of cost-effective diagnostics as a viable
avenue. The widespread adoption of smartphones in society has precipitated
their integration into the realm of medical diagnostics, marking an expanding
trend [29, 30]. The ubiquity and affordability of high-pixel-density
smartphone cameras have garnered recognition for their imaging capabilities
across diverse scientific domains [31, 32, 33, 34]. Second, the dataset
developed as part of this research, offered under open data licensing,
contributes further to the realm of explainable artificial intelligence (XAI)
in medical imaging. The availability of such a dataset could potentially
foster innovation, contributing to the advancement of both medical and
technological applications.

##  3 Methodology

In this section, we present our novel system, HemaX and set forth a technique
for establishing the faithfulness of explanations.

###  3.1 HemaX

Figure 2: Architecture of HemaX, an end-to-end model for explainabilty of WBC.
The transformer encoder-decoder attends to the image features extracted by the
ResNet-50 backbone and forwards it to the FFNNs. The FFNNs perform
classification, localization, and feature prediction simultaneously. The
segmentation maps are computed separately for the nucleus and cytoplasm
region. The highlighted region in the figure represents the DETR panoptic
segmentation architecture.

Transformer-based models have shown impressive and elegant results in several
medical image analysis tasks [35, 36, 37, 38, 39, 40]. The proposed HemaX
model extends the DETR panoptic segmentation [41]. HemaX (Figure 2) yields
three outputs similar to DETR (class labels, bounding box coordinates and a
segmentation mask) as well as an extra branch that outputs explanations
corresponding to the predicted class. HemaX adopts the components of DETR: a
ResNet-50 model pre-trained on Imagenet weight as the backbone convolutional
neural network (CNN) encoder, six transformer encoder-decoder blocks, and the
two prediction heads for class prediction and bounding box detection. To
obtain the segmentation mask we have modified the feature pyramid network
(FPN) based CNN decoder to predict a two-channel segmentation mask for each of
the $N$ object queries. The two channels contain the predicted cytoplasm and
nucleus mask, respectively. The N:C ratio is computed based on the number of
pixels occupied by the nucleus and cytoplasm in their corresponding
segmentation masks. We have also redirected the transformer decoder’s output
to a feed-forward network (FFN) that predicts other four
explanations—granularity, cytoplasm color, nucleus shape, and size with
respect to red blood cells.
HemaX employs a composite loss function$(\mathcal{L})$, consisting of four
losses (prediction loss, bounding box loss, segmentation loss and _explanation
loss_). For a given image, HemaX produces N predictions where N is larger than
the number of groundtruthed objects in the image. Initially, the groundtruthed
set of objects is optimally matched to a predicted set with a permutation
index $\sigma_{i}$ ($\sigma\in\mathfrak{S}_{N}$) using Hungarian algorithm[41,
42]. The class prediction and the similarity between predicted and
groundtruthed boxes are both taken into consideration by the matching cost.
Following this, we calculate the composite loss of the model. The composite
loss function $\mathcal{L}$ is given in Equation 1 where $\varnothing$ denotes
the empty class.

$\mathcal{L}=\sum_{i=1}^{N}\\{\mathcal{L}_{p}+\mathds{1}_{\\{p_{i}\neq\varnothing\\}}\mathcal{L}_{b}+\mathds{1}_{\\{p_{i}\neq\varnothing\\}}\mathcal{L}_{s}+\mathds{1}_{\\{p_{i}\neq\varnothing\\}}\mathcal{L}_{e}\\}$
(1)

The first term of the loss is the prediction loss $\mathcal{L}_{p}$. Let
$c_{i}$ be the true object class and $\hat{p}_{\sigma_{i}}(c_{i})$ be the
probability of class $c_{i}$ for the prediction with index $\sigma_{i}$. The
prediction loss is given by
$\mathcal{L}_{p}=-\log\hat{p}_{\sigma_{i}}(c_{i})$. The second term, i.e., the
bounding box loss $\mathcal{L}_{b}$ is the linear combination of two losses:
L1 and generalized intersection-over-union(GIoU) loss [43]. The bounding box
loss between the true bounding box $b_{i}$ and the predicted bounding box
$\hat{b}_{\sigma_{i}}$ is defined as
$\mathcal{L}_{b}(b_{i},\hat{b}_{\sigma_{i}})=w_{GIoU}\mathcal{L}_{GIoU}(b_{i},\hat{b}_{\sigma_{i}})+w_{L1}||b_{i}-\hat{b}_{\sigma_{i}}||_{1}$,
where $w_{GIoU}$ and $w_{L1}$ denote the weights of GIoU loss and L1 loss
respectively. The third term, segmentation loss $\mathcal{L}_{s}$, is the
weighted sum of two losses namely focal loss(FL) and Sørensen-Dice loss(dice)
[44]. The segmentation loss between actual mask $s_{i}$ and predicted mask
$\hat{s}_{\sigma_{i}}$ is measured by
$\mathcal{L}_{s}(s_{i},\hat{s}_{\sigma_{i}})=w_{dice}\mathcal{L}_{dice}(s_{i},\hat{s}_{\sigma_{i}})+w_{FL}\mathcal{L}_{FL}(s_{i},\hat{s}_{\sigma_{i}})$,
where $w_{dice}$ and $w_{FL}$ denote the weights of Sørensen Dice loss and
focal loss respectively. To compute the fourth term, i.e, explanation loss,
let $E=(a_{1},a_{2},a_{3},a_{4})_{i=1}^{N}$ be the groundtruthed explanation,
where $a_{k}$ denotes an attribute value and $A_{k}$ denotes the range of
values for the $k$-th attribute. Note that four attributes namely,
granularity, cytoplasm color, nucleus shape and size w.r.t red blood cells are
considered here and the fifth attribute (i.e., N:C ratio) of the explanation
is computed from the segmentation mask and it is not part of
$\mathcal{L}_{e}$. Let
$\hat{E}_{\sigma_{i}}=(\hat{a}_{1},\hat{a}_{2},\hat{a}_{3},\hat{a}_{4})_{i=1}^{N}$
denote the set of predicted explanations. The explanation loss
$\mathcal{L}_{e}(E_{i},\hat{E}_{\sigma_{i}})$ is given as the weighted sum of
attribute-wise cross-entropy loss (Equation 2) where $w_{i}$ represents the
weight corresponding to the $i$-th attribute.

$\mathcal{L}_{e}(E_{i},\hat{E}_{\sigma_{i}})=\sum_{i=1}^{4}w_{i}\Bigg{(}-\sum_{a_{j}\in
A_{i}}a_{j}\log\hat{a}_{j}\Bigg{)}$ (2)

###  3.2 Faithfulness of HemaX’s predictions

Aside from the evaluation of performance using various metrics, we also
emphasise on generating ‘faithful’ explanations aligned with the
classifications. Our evaluation of explanation faithfulness involves comparing
the model-induced association $\hat{A}$ (between the classification output and
the explanation produced/induced by our model) with the ground truth
association $A$ (between the classification target and the true explanation
from the dataset). This assessment of faithfulness draws inspiration from the
concept of strong rules proposed by Agrawal et al. [45]. Associations are
characterized through conditional probability distributions
($\operatorname{Pr}(\cdot|\cdot)$) of explanations (as described in Equation3
and 4).

$\displaystyle\hat{A}$ $\displaystyle\triangleq
p_{\hat{A}}=\operatorname{Pr}(E|C=\hat{c})$ (3) $\displaystyle A$
$\displaystyle\triangleq p_{A}=\operatorname{Pr}(E|C=c)$ (4)

where, $E$ is the explanation, $\hat{c}$ is the predicted output from HemaX
and $c$ is the ground truth. When dissimilar associations
($p_{\hat{A}}\not\approx p_{A}$) arise, the faithfulness of explanations
produced by our model is significantly compromised.

##  4 Results and Discussion

In this section, we provide the details regarding baselines, datasets,
training particulars and the evaluation metrics used. Additionally, we outline
the process of selecting the best HemaX model and its subsequent comparison
with the other competing systems. We also present the plots to establish the
faithfulness of explanations as well as show the independence between
predicted cell type and explanation.

Figure 3: Output on two WBC images using HemaX with no hidden layer.

###  4.1 Baseline Implementation Details

The proposed network, HemaX, and all the other networks (Faster RCNN and CUSS-
Net) are implemented in PyTorch and are trained using a single Nvidia RTX
A6000 GPU with 48GB memory. HemaX is trained using AdamW optimizer [46] with a
weight decay of $1e-4$ for $N=10$ object queries. The learning rate of the
backbone CNN encoder, initialized with ResNet 50 model pre-trained on the
ImageNet dataset, is set at $1e-5$, whereas the remainder of the model is
trained using a learning rate of $1e-4$. Faster RCNN is trained by
initializing its backbone with a ResNet 50 model pre-trained on the ImageNet
dataset. The network is trained using the Stochastic Gradient Descent (SGD)
optimizer with a learning rate of $5e-3$. CUSS-Net is trained using AdamW
optimizer with a learning rate of $1e-4$. In our case, we have used CUSS-Net
without the unsupervised-based strategy module. All three networks: HemaX,
Faster RCNN, and CUSS-Net, are trained for $200$ epochs with a batch size of
$32$ and the model corresponding to the lowest validation loss is chosen for
evaluation. Tavakoli’s algorithm is implemented by selecting the polynomial
kernel for the support vector machine (SVM) classifier.

###  4.2 Datasets and Training Details

In our study, we primarily utilized three datasets. Let’s begin with the
LeukoX dataset, the details of which have already been discussed in the
Section 2. The $467$ images in the LeukoX dataset were divided randomly into
training and test sets, with an $80\%$-$20\%$ ratio.

Moving on to the _LISC_ dataset [2], it comprises $250$ blood smear images
annotated by a single expert. These slides were stained using the Gismo-Right
technique and captured using a light microscope with a $100$ magnification.
For our experiments, we partitioned the LISC images into training and test
sets, using a $90\%$-$10\%$ ratio.

The _Raabin_ dataset [28] encompasses $1145$ images annotated with nuclei and
cytoplasm details. Blood films were stained using the Giemsa technique and
captured using an Olympus CX18 microscope with a $100$ magnification. Cameras
from Samsung Galaxy S5 and LG G3 were used for image capture. Out of the total
images, $233$ were designated as test images, while the rest were allocated
for training purposes.

For assessing our models’ performance, we employed five-fold cross-validation.
Additionally, to expand the training dataset and introduce variety, we
utilized standard data augmentation techniques, including scaling, rotation,
translation, horizontal flipping, and vertical flipping.

###  4.3 Evaluation Metrics

We assess the performance of HemaX across various aspects, including
classification, bounding box localization, segmentation mask, and explanation
prediction. For the classification evaluation, we utilize precision,
$F_{1}$-score, and accuracy metrics. To evaluate bounding boxes, we calculate
the Jaccard index [47], averaging it over each bounding box instance. For the
segmentation masks, we use the Sørensen dice coefficient [48] to evaluate each
object instance’s quality and then average it over all object instances.
Regarding the quality of explanations, we employ the accuracy measure for
categorical attributes (e.g., granularity, cytoplasm color, nucleus shape, and
size in relation to red blood cells). Additionally, we use the mean square
error (MSE) to assess the accuracy of the N:C ratio explanation.

###  4.4 Variants of HemaX

Metrics | Number of Hidden Layers
---|---
$0$ | $2$ | $4$
Dice Score | 0.9270 $\pm$ 0.0020 | 0.9213 $\pm$ 0.0056 | 0.9266 $\pm$ 0.0022
Jaccard Ind. | 0.8916 $\pm$ 0.0160 | 0.8878 $\pm$ 0.0084 | 0.8871 $\pm$ 0.0130
Class. acc. | 0.8108 $\pm$ 0.0161 | 0.7978 $\pm$ 0.0258 | 0.8065 $\pm$ 0.0180
Precision | 0.8351 $\pm$ 0.0088 | 0.8225 $\pm$ 0.0246 | 0.8391 $\pm$ 0.0178
F1 score | 0.8089 $\pm$ 0.0162 | 0.7939 $\pm$ 0.0253 | 0.8060 $\pm$ 0.0185

Table 1: The scores for segmentation and classification metrics on variants of
HemaX.

Attributes | Number of Hidden Layers
---|---
$0$ | $2$ | $4$
Gran. acc. | 0.9355 $\pm$ 0.0096 | 0.9204 $\pm$ 0.0086 | 0.9355 $\pm$ 0.0167
Cytoplasm col. acc. | 0.8258 $\pm$ 0.0219 | 0.8366 $\pm$ 0.0322 | 0.8172 $\pm$ 0.0353
Nucleus shape acc. | 0.8495 $\pm$ 0.0326 | 0.8430 $\pm$ 0.0277 | 0.8194 $\pm$ 0.0315
Size wrt RBC acc. | 0.8710 $\pm$ 0.0180 | 0.8753 $\pm$ 0.0146 | 0.8839 $\pm$ 0.0230
N:C MSE | 0.0317 $\pm$ 0.0028 | 0.0316 $\pm$ 0.0023 | 0.0312 $\pm$ 0.0028

Table 2: The accuracy and MSE scores of various explanations on variants of
HemaX.

(a)

(b)

(c)

(d)

(e)

Figure 4: (a), (b), (c) and (d) shows the category-wise ROC curves for
granularity, cytoplasm color, nucleus shape, and size w.r.t RBCs respectively.
(e) shows the classwise MSE loss for 10 cell types.

We experimented with the proposed network by adding a different number of
hidden layers to its explanation prediction branch and the results are
analyzed using the metrics outlined in Section 4.3. The classification and
segmentation performance of HemaX with various configurations in the
explanation prediction branch is shown in Table 2. The performance of HemaX’s
explanation prediction is illustrated in Table 2. The best result
corresponding to each evaluation metric is marked in bold. The comparison
between the variants of HemaX shows that by varying the number of hidden
layers, we achieve similar performance in terms of classification metrics
(Dice score, Jaccard index, classification accuracy, precision, and $F_{1}$
score). However on decreasing the number of hidden layers from four to zero,
HemaX achieves a boost of $3\%$ accuracy in nucleus shape prediction and an
increment of $\approx 1\%$ accuracy in predicting cytoplasm color. The HemaX
variant with no hidden layers in the explanation prediction branch is selected
as the best-performing model based on the $F_{1}$ score. Thus, we have
empirically demonstrated that HemaX possesses the capability to decipher the
characteristics of colored WBC images through five explanations: granularity,
cytoplasm color, nucleus shape, size relative to red blood cells, and the
nucleus-to-cytoplasm ratio. An illustrative outcome of HemaX, applied to two
sample images, is presented in Fig. 3. The segmentation masks predicted by
HemaX showcase its capability to discern the nucleus from the cytoplasm,
despite the granular nature of the cells. Additionally, we have utilized the
top-performing HemaX model to generate Receiver Operator Characteristic (ROC)
curves for the four explanations (Figures 4.(a), 4.(b), 4.(c), and 4.(d)). The
ROC curve plots demonstrate that HemaX achieves an area under the curve (AUC)
greater than 0.85 for each sub-explanation, indicating the model’s precise
ability to distinguish between different explanations. Additionally, we
plotted the classwise normalized mean square error (MSE) for the N:C ratio
(Figure 4.(e)). The classwise normalized MSE scores for the N:C ratio reveal
that blast cells have the lowest MSE (0.0002), while promyelocytes have the
highest MSE (0.2494).

###  4.5 Comparison with other Leukocyte Classification Models

Architecture | Classification acc. | Precision | F1 score
---|---|---|---
Faster-RCNN [27] | 0.7226 $\pm$ 0.0749 | 0.7494 $\pm$ 0.0850 | 0.7130 $\pm$ 0.0897
Tavakoli’s algorithm [7] | 0.5441 $\pm$ 0.0285 | 0.5452 $\pm$ 0.0356 | 0.5372 $\pm$ 0.0288
CUSS-Net [16] | 0.7161 $\pm$ 0.0364 | 0.7340 $\pm$ 0.0511 | 0.7077 $\pm$ 0.0418
HemaX (0-Hidden Layers) | 0.8108 $\pm$ 0.0161 | 0.8351 $\pm$ 0.0088 | 0.8089 $\pm$ 0.0162

Table 3: The classification metrics corresponding to various techniques on
LeukoX dataset

We continue to assess the capabilities of HemaX by comparing it with the
state-of-the-art classification techniques, including Faster RCNN, CUSS-Net,
and Tavakoli’s algorithm. Metrics corresponding to the four classification
techniques are presented in Table 3. HemaX stands out with an classification
accuracy of 0.8108, precision of 0.8351, and $F_{1}$-score of 0.8089,
surpassing the performance of the other three best known techniques.
Furthermore, this experiment demonstrates that HemaX can simultaneously
classify cells and provide explanations without experiencing a significant
drop in performance.

Architecture | Classification acc. | Precision | F1 score
---|---|---|---
Faster-RCNN [27] | 0.9039 $\pm$ 0.0192 | 0.9840 $\pm$ 0.0161 | 0.9357 $\pm$ 0.0194
Tavakoli’s algorithm [7] | 0.9423 $\pm$ 0.0192 | 0.9506 $\pm$ 0.0175 | 0.9404 $\pm$ 0.0208
CUSS-Net [16] | 0.9896 $\pm$ 0.0181 | 0.9913 $\pm$ 0.0150 | 0.9895 $\pm$ 0.0182
HemaX (No explanation branch) | 0.9808 $\pm$ 0.0192 | 0.9839 $\pm$ 0.0161 | 0.9806 $\pm$ 0.0195

Table 4: The classification metrics corresponding to various techniques on
LISC dataset.

Architecture | Classification acc. | Precision | F1 score
---|---|---|---
Faster-RCNN [27] | 0.9335 $\pm$ 0.0168 | 0.9371 $\pm$ 0.0157 | 0.9331 $\pm$ 0.0170
Tavakoli’s algorithm [7] | 0.8809 $\pm$ 0.0131 | 0.8852 $\pm$ 0.0127 | 0.8813 $\pm$ 0.0129
CUSS-Net [16] | 0.9614 $\pm$ 0.0068 | 0.9618 $\pm$ 0.0071 | 0.9612 $\pm$ 0.0071
HemaX (No explanation branch) | 0.9596 $\pm$ 0.0048 | 0.9603 $\pm$ 0.0047 | 0.9595 $\pm$ 0.0047

Table 5: The classification metrics corresponding to various techniques on
Raabin dataset.

To demonstrate its generalizability, we compared HemaX (without its
explanation prediction branch) with three other state-of-the-art techniques
using two publicly available datasets: LISC [2] and Raabin [28]. Detailed
information about these datasets is provided in Section 4.2. The performance
of different techniques on the LISC dataset is summarized in Table 4, while
Table 5 presents the performance of various techniques on the Raabin dataset.

Compared to other state-of-the-art approaches (Faster R-CNN, CUSS-Net, and
Tavakoli’s algorithm), HemaX exhibits several advantages. Contrary to Faster
RCNN’s two-step process, which involves first proposing regions followed by
detecting objects, HemaX accomplishes one-step detection by directly
predicting a group of candidate objects and their corresponding bounding box
for the given input image. The experimental results highlight that this
single-phase detection approach leads to an $8.32\%$, $4.49\%$, and $2.64\%$
improvement in $F_{1}$-score on the LeukoX, LISC, and Raabin datasets,
respectively, compared to Faster RCNN. Moreover, HemaX leverages attention
maps to emphasize relevant parts of the input image, resulting in superior
performance compared to CUSS-Net in the LeukoX dataset and similar performance
on the two publicly available datasets.

Our study presents significant advancements compared to prior research,
contributing both in terms of translation and methodology. From a
translational perspective, we have demonstrated HemaX’s strong generalization
capabilities using two publicly available WBC datasets. This robust
performance in WBC classification, coupled with its ability to provide
explanations, is expected to bolster pathologists’ confidence in the
reliability of HemaX. As a result, there is a higher likelihood of its
widespread adoption. This is in contrast to previous studies that solely
evaluated AI systems for WBC classification, often leaving their inner
workings opaque. In terms of methodology, HemaX is on par with previously
developed AI systems, which were primarily designed for WBC classification.
The key advantage of HemaX lies in its unified approach, enabling it to
seamlessly perform multiple tasks without necessitating fundamental
architectural changes. This distinguishes HemaX from the aforementioned prior
systems and highlights its versatility.

###  4.6 Faithfulness of Predictions

Figure 5: Comparison between model-induced association ($\hat{A}$) and ground
truth association ($A$) for all four explanations (‘Granularity’, ‘Cytoplasm
Color’, ‘Nucleus Shape’ and ‘Size w.r.t. RBC’) on ‘Metamyelocytes’ cell type.

Figure 6: Comparison between model-induced association ($\hat{A}$) and ground
truth association ($A$) for all four explanations (‘Granularity’, ‘Cytoplasm
Color’, ‘Nucleus Shape’ and ‘Size w.r.t. RBC’) on ‘Neutrophils’ cell type.

Figure 7: Comparison between model-induced association ($\hat{A}$) and ground
truth association ($A$) for all four explanations (‘Granularity’, ‘Cytoplasm
Color’, ‘Nucleus Shape’ and ‘Size w.r.t. RBC’) on ‘Promyelocyte’ cell type.

We have established the faithfulness of explanation predictions by the best
HemaX model using the technique described in Section 3.2. The Figures 5, 6 and
7 depicts $\hat{A}$ and $A$ for explanations based on four explanations:
‘Granularity’, ‘Cytoplasm color’, ‘Nucleus shape’ and ‘Size w.r.t. RBC’,
focusing on Metamyelocyte, Neutrophil and Promyelocyte cell types. The figure
clearly illustrates that both the model-induced and ground truth associations
($p_{\hat{A}}$ and $p_{A}$) exhibit a high degree of similarity. This
resemblance provides evidence for the faithfulness of the four explanations
(‘Granularity’, ‘Cytoplasm color’, ‘Nucleus shape’ and ‘Size w.r.t. RBC’)
generated by HemaX. Similar results have been achieved for other cell types on
four explanations (listed in the supplemental note).

###  4.7 Independence of Cell Classification and Explanation

We have also conducted an investigation to ascertain whether the explanations
were influenced by the predicted cell type, using two methods: comparing the
Area Under the Curves (AUCs) for various explanations on the LeukoX dataset
and validating the results with an expert hematologist. First, the AUCs for
each explanation were computed separately for both correctly and incorrectly
classified WBCs, as outlined in Table 6. This analysis yielded two significant
observations: (1) The AUCs for the explanations of correctly classified WBCs
were consistently higher than the explanations for the misclassified ones and
(2) Even when the cell type prediction was inaccurate, all the explanations
still exhibited AUC values exceeding 0.60. This indicates the model’s
proficiency in accurately capturing the explanatory aspects, regardless of the
classification outcome.

Explanation Type | Explanation | AUC for
---|---|---
Classified WBCs | Misclassified WBCs
Granularity | Yes | 0.99 | 0.60
No | 0.99 | 0.60
Cytoplasm Color | Eosinophilic | 0.93 | 0.77
Basophilic | 0.93 | 0.77
Nucleus Shape | Horseshoe/ Kidney Bean | 0.93 | 0.84
Bilobed/ Multilobed | 0.97 | 0.95
Round/ Oval | 0.95 | 0.92
Size w.r.t. RBCs | Larger | 0.88 | 0.82
Nearly Similar | 0.89 | 0.68
Smaller | $-$ | $-$

Table 6: The AUC values for each explanation predicted by the best HemaX model

Furthermore, explanations from the best HemaX model were validated by a senior
hematologist. As per the hematologist’s assessment, the correct predictions
for the granularity attribute were $90.91\%$, for cytoplasm color they were
$84.42\%$, for nucleus shapes they were $93.51\%$, and for size with respect
to RBC they were $88.31\%$, all pertaining to correctly classified WBCs. In
the case of misclassified WBCs, the correct predictions for granularity,
cytoplasm color, nucleus shape, and size with respect to RBC were $87.50\%$,
$75.00\%$, $87.50\%$, and $81.25\%$, respectively. What’s particularly
noteworthy is that even for misclassified cell types, the system generated
explanations of high quality, achieving validation accuracy of over $80\%$ for
all four attributes. This observation underscores the model’s ability to
independently learn both the classification task and explanation prediction,
highlighting its robustness and efficacy in handling both objectives
concurrently. The N:C ratio is similarly learned independently, as it is
determined using the segmentation masks for the nucleus and cytoplasm.

##  5 Conclusion

In this study, we developed a transformer encoder-decoder based network,
HemaX, for classifying WBCs and predicting human-centric explanations for the
classified cell simultaneously. The experimental results show that HemaX is
quite efficient in serving a dual purpose: classifying White Blood Cells
(WBCs) and concurrently predicting human-centric explanations for the
classified cells. As far as we are aware, HemaX stands as the pioneering
explainable architecture tailored for white blood cell classification. Given
that clinical practice relies heavily on a vast knowledge foundation, any
computer-aided application must provide machine decisions in a comprehensible
format to gain acceptance among medical professionals. This paper adds novelty
to the ongoing research and shows a way of integrating an expert knowledge
base into a learning system. The dataset which will be available under open
data licensing is another contribution of this research towards explainable
artificial intelligence (XAI) in medical imaging. Though further research is
needed to improve the accuracy of feature-level interpretation, the system’s
ability to predict hematologist-like explanations would make it more appealing
to medical practitioners.

One of the limitations of our research pertains to the utilization of a
dataset featuring single-cell WBC images for explanation prediction. To extend
the exploration of HemaX’s capability for predicting explanations across
multiple cell instances and to ensure its robust applicability in pathology
laboratories, we encourage the public release of comprehensive explainable WBC
datasets. These datasets should encompass multiple cells per image and be
sourced from various hospitals and pathological laboratories. By combining
such datasets with HemaX, researchers can unlock the potential for generating
human-centric explanations on a larger scale, thus fostering more extensive
insights.

Although our focus has been on employing HemaX to predict the five human-
centric explanations for each cell, the potential exists for its application
to explore other cell explanations across various cell types. The exploration
of additional cell explanations could be realized by curating a dataset with
annotations specific to the desired property. We are making our code
accessible to enable others to utilize HemaX and gain insights into other
cellular properties. Looking ahead, our future focus will revolve around
examining the impact of HemaX on clinical stakeholders. These investigations
will yield valuable practical insights into the effectiveness of AI-based
systems in real-world scenarios, shedding light on their reception and
perception by clinical experts. This endeavor promises to enrich our
understanding of the translation of AI innovations into clinical practice.

## Acknowledgment

This research is supported by the Science and Engineering Research Board
(SERB), Dept. of Science and Technology (DST), Govt. of India through Grant
File No. SPR/2020/000495.

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Supplementary Material

Pathologist-Like Explanations Unveiled: an Explainable Deep Learning System
for White Blood Cell Classification

## Part-1: Graphs for Faithfulness of Explanations

In the discussion section of the paper, we show the comparison between model-
induced association ($\hat{A}$) and ground truth association ($A$) for four
explanations only on two among the ten cell types. Here we provide such
comparision for rest of the eight (8) cell type, namely—‘Band Cells’ (Fig. 8),
‘Basophils’ (Fig. 9), ‘Blast Cells’ (Fig. 10), ‘Eosinophils’ (Fig. 11),
‘Lymphocytes’ (Fig. 12), ‘Monocytes’ (Fig. 13), and ‘Myelocytes’ (Fig. 14).
All these figures support the conclusion that we reached in the discussion
section regarding the faithfulness of explanation prediction.

Figure 8: Comparison between model-induced association ($\hat{A}$) and ground
truth association ($A$) for all four explanations (‘Granularity’, ‘Cytoplasm
Color’, ‘Nucleus Shape’ and ‘Size w.r.t. RBC’) on ‘Band Cell’ cell type.

Figure 9: Comparison between model-induced association ($\hat{A}$) and ground
truth association ($A$) for all four explanations (‘Granularity’, ‘Cytoplasm
Color’, ‘Nucleus Shape’ and ‘Size w.r.t. RBC’) on ‘Basophil’ cell type.

Figure 10: Comparison between model-induced association ($\hat{A}$) and ground
truth association ($A$) for all four explanations (‘Granularity’, ‘Cytoplasm
Color’, ‘Nucleus Shape’ and ‘Size w.r.t. RBC’) on ‘Blast Cell’ cell type.

Figure 11: Comparison between model-induced association ($\hat{A}$) and ground
truth association ($A$) for all four explanations (‘Granularity’, ‘Cytoplasm
Color’, ‘Nucleus Shape’ and ‘Size w.r.t. RBC’) on ‘Eosinophil’ cell type.

Figure 12: Comparison between model-induced association ($\hat{A}$) and ground
truth association ($A$) for all four explanations (‘Granularity’, ‘Cytoplasm
Color’, ‘Nucleus Shape’ and ‘Size w.r.t. RBC’) on ‘Lymphocyte’ cell type.

Figure 13: Comparison between model-induced association ($\hat{A}$) and ground
truth association ($A$) for all four explanations (‘Granularity’, ‘Cytoplasm
Color’, ‘Nucleus Shape’ and ‘Size w.r.t. RBC’) on ‘Monocyte’ cell type.

Figure 14: Comparison between model-induced association ($\hat{A}$) and ground
truth association ($A$) for all four explanations (‘Granularity’, ‘Cytoplasm
Color’, ‘Nucleus Shape’ and ‘Size w.r.t. RBC’) on ‘Myelocyte’ cell type.

## Part-2: HemaX Results

Figure 15: Results on LeukoX dataset: We present HemaX’s output on sample
images from LeukoX dataset. The misclassified results are marked in blue.

Figure 15 shows some additional results on LeukoX produced by HemaX. The
initial two rows exhibit accurately predicted WBCs with their accompanying
explanations, while the subsequent three rows demonstrate instances where the
model failed. In the third and fourth rows, examples are presented where the
top-performing HemaX model misclassified labels but still provided accurate
explanations for the cells.