# An Explainable AI System for Automated COVID-19 Assessment and Lesion
Categorization from CT-scans

Matteo Pennisi111DIEEI, University of Catania, Catania, Italy Isaak
Kavasidis22footnotemark: 2 Concetto Spampinato33footnotemark: 3 Vincenzo
Schininà444National Institute for infectious disease, “Lazzaro Spallanzani”
Department, Rome, Italy Simone Palazzo55footnotemark: 5 Francesco
Rundo666STMicroelectronics - ADG Central R&D, Catania, Italy Massimo
Cristofaro77footnotemark: 7 Paolo Campioni88footnotemark: 8 Elisa
Pianura99footnotemark: 9 Federica Di Stefano1010footnotemark: 10 Ada
Petrone1111footnotemark: 11 Fabrizio Albarello1212footnotemark: 12 Giuseppe
Ippolito1313footnotemark: 13 Salvatore Cuzzocrea141414ChimBioFaram Department,
University of Messina, Messina, Italy Sabrina Conoci1515footnotemark: 15

(November 2020)

###### Abstract

COVID-19 infection caused by SARS-CoV-2 pathogen is a catastrophic pandemic
outbreak all over the world with exponential increasing of confirmed cases
and, unfortunately, deaths. In this work we propose an AI-powered pipeline,
based on the deep-learning paradigm, for automated COVID-19 detection and
lesion categorization from CT scans. We first propose a new segmentation
module aimed at identifying automatically lung parenchyma and lobes. Next, we
combined such segmentation network with classification networks for COVID-19
identification and lesion categorization. We compare the obtained
classification results with those obtained by three expert radiologists on a
dataset consisting of 162 CT scans. Results showed a sensitivity of 90% and a
specificity of 93.5% for COVID-19 detection, outperforming those yielded by
the expert radiologists, and an average lesion categorization accuracy of over
84%. Results also show that a significant role is played by prior lung and
lobe segmentation that allowed us to enhance performance by over 20 percent
points. The interpretation of the trained AI models, moreover, reveals that
the most significant areas for supporting the decision on COVID-19
identification are consistent with the lesions clinically associated to the
virus, i.e., crazy paving, consolidation and ground glass. This means that the
artificial models are able to discriminate a positive patient from a negative
one (both controls and patients with interstitial pneumonia tested negative to
COVID) by evaluating the presence of those lesions into CT scans. Finally, the
AI models are integrated into a user-friendly GUI to support AI explainability
for radiologists, which is publicly available at http://perceivelab.com/covid-
ai. The whole AI system is unique since, to the best of our knowledge, it is
the first AI-based software, publicly available, that attempts to explain to
radiologists what information is used by AI methods for making decision and
that involves proactively them in the decision loop to further improve the
COVID-19 understanding.

##  1 Introduction

At the end of 2019 in Wuhan (China) several cases of an atypical pneumonia,
particularly resistant to the traditional pharmacological treatments, were
observed. In early 2020, the COVID-19 virus [1] has been identified as the
responsible pathogen for the unusual pneumonia. From that time, COVID-19 has
spread all around the world hitting, to date about 32 million of people (with
about 1M deaths), stressing significantly healthcare systems in several
countries. Since the beginning, it has been noted that 20% of infected
subjects appear to progress to severe disease, including pneumonia and
respiratory failure and in around 2% of cases death [2].

Currently, the standard diagnosis of COVID-19 is de facto based on a
biomolecular test through Real-Time Polimerase Chain Reaction (RT-PCR) test
[3, 4]. However, although widely used, this biomolecular method is time-
consuming and appears to be not quite accurate suffering from a large number
of false-negatives [5].

Recent studies have outlined the effectiveness of radiology imaging through
chest X-ray and mainly Computed Tomography (CT) given the pulmonary
involvement in subjects affected by the infection [5, 6]. Given the extension
of the infection and the number of cases that daily emerge worldwide and that
call for fast, robust and medically sustainable diagnosis, CT scan appears to
be suitable for a robust-scale screening, given the higher resolution w.r.t.
X-Ray. In this scenario, artificial intelligence may play a fundamental role
to make the whole diagnosis process automatic, reducing, at the same time, the
efforts required by radiologists for visual inspection [7].

In this paper, thus, we present an innovative artificial intelligent approach
to achieve both COVID-19 identification and lesion categorization (ground
glass, crazy and paving consolidation) that are instrumental to evaluate lung
damages and the prognosis assessment. Our method relies only on radiological
image data avoiding the use of additional clinical data in order to create AI
models that are useful for large-scale and fast screening with all the
subsequent benefits for a favorable outcome. More specifically, we propose an
innovative automated pipeline consisting of 1) lung/lobe segmentation, 2)
COVID-19 identification and interpretation and 3) lesion categorization. We
tested the AI-empowered software pipeline on multiple CT scans, both publicly
released and collected at the Spallanzani Institute in Italy, and showed that:
1) our segmentation networks is able to effectively extract lung parenchyma
and lobes from CT scans, outperforming state of the art models; 2) the
COVID-19 identification module yields better accuracy (as well as specificity
and sensitivity) than expert radiologists. Furthermore, when attempting to
interpret the decisions made by the proposed AI model, we found that it
learned automatically, and without any supervision, the CT scan features
corresponding to the three most common lesions spotted in the COVID-19
pneumonia, i.e., consolidation, ground glass and crazy paving, demonstrating
its reliability in supporting the diagnosis by using only radiological images.
As an additional contribution, we integrate the tested AI models into an user-
friendly GUI to support further AI explainability for radiologists, which is
publicly available at http://perceivelab.com/covid-ai. The GUI processes
entire CT scans and reports if the patient is likely to be affected by
COVID-19, showing, at the same time, the scan slices that supported the
decision.

##  2 Related Work

The COVID-19 epidemic caught the scientific community flat-footed and in
response a high volume of research has been dedicated at all possible levels.
In particular, since the beginning of the epidemic, AI models have been
employed for disease spread monitoring [8, 9, 10], for disease progression
[11] and prognosis [12], for predicting mental health ailments inflicted upon
healthcare workers [13] and for drug repurposing [14, 15] and discovery [16].

However, the lion’s share in employing AI models for the fight against
COVID-19 belongs to the processing of X-rays and CT scans with the purpose of
detecting the presence of COVID-19 or not. In fact, recent scientific
literature has demonstrated the high discriminative and predictive capability
of deep learning methods in the analysis of COVID-19 related radiological
images[17, 18]. The key radiological techniques for COVID-19 induced pneumonia
diagnosis and progression estimation are based on the analysis of CT and X-ray
images of the chest, on which deep learning methodologies have been widely
used with good results for segmentation, predictive analysis, and
discrimination of patterns [19, 20, 21]. If, on one hand, X-Ray represents a
cheaper and most effective solution for large scale screening of COVID-19
disease, on the other hand, its low resolution has led AI models to show lower
accuracy compared to those obtained with CT data.

For the above reasons, CT scan has become the gold standard for investigation
on lung diseases. In particular, deep learning, mainly in the form of Deep
Convolutional Neural Networks (DCNN), has been largely applied to lung disease
analysis from CT scans images, for evaluating progression in response to
specific treatment (for instance immunotherapy, chemotherapy, radiotherapy)
[22, 23], but also for interstitial lung pattern analysis [24, 25] and on
segmentation and discrimination of lung pleural tissues and lymph-nodes [26,
27]. This latter aspect is particularly relevant for COVID-19 features and
makes artificial intelligence an extremely powerful tool for supporting early
diagnosis of COVID-19 and disease progression quantification. As a
consequence, several recent works have reported using AI models for automated
categorization of CT scans [21] and also on COVID-19 [28, 29, 30] but without
being able to distinguish between the various types of COVID-19 lesions.

Thus, the main contributions of this paper w.r.t. the state of the art are the
following ones:

  * 1.

We propose a novel lung-lobe segmentation network outperforming state of the
art models;

  * 2.

We employ the segmentation network to drive a classification network in first
identifying CT scans of COVID-19 patients, and, afterwards, in automatically
categorizing specific lesions;

  * 3.

We then provide interpretation of the decisions made by the employed models
and discover that, indeed, those models focus on specific COVID-19 lesions for
distinguishing whether a CT scan pertains COVID-19 patients or not;

  * 4.

We finally integrate the whole AI pipeline into a web platform to ease use for
radiologists, supporting them in their investigation on COVID-19 disease.

##  3 Explainable AI for COVID-19 data understanding

The proposed AI system aims at 1) extracting lung and lobes from chest CT
data, 2) categorizing CT scans as either COVID-19 positive or COVID-19
negative; 3) identifying and localizing typical COVID-19 lung lesions
(consolidation, crazy paving and ground glass); and 4) explaining eventually
what CT slices it based its own decisions.

###  3.1 AI Model for Lung Segmentation

Our lung-lobe segmentation model is based on the _Tiramisu_ network [31], a
fully-convolutional DenseNet [32] in a U-Net architecture [33]. The model
consists in two data paths: the downsampling one, that aims at extracting
features and the upsampling one that aims at generating the output images
(masks). Skip connections (i.e., connections starting from a preceding layer
in the network’s pipeline to another one found later bypassing intermediate
layers) aim at propagating high-resolution details by sharing feature maps
between the two paths.

In this work, our segmentation model follows the Tiramisu architecture, but
with two main differences:

  * 1.

Instead of processing each single scan individually, convolutional LSTMs [34]
are employed at the network’s bottleneck layer to exploit the spatial axial
correlation of consecutive scan slices.

  * 2.

In the downsampling and upsampling paths, we add residual squeeze-and-
excitation layers [35], in order to emphasize relevant features and improve
the representational power of the model.

Before discussing the properties and advantages of the above modifications, we
first introduce the overall architecture, shown in Fig. 1.

Figure 1: The proposed segmentation architecture, consisting of a downsampling
path (top) and an upsampling path (bottom), interconnected by skip connections
and by the bottleneck layer.

The input to the model is a sequence of 3 consecutive slices – suitably
resized to 224$\times$224 – of a CT scan, which are processed individually and
combined through a convolutional LSTM layer. Each slice is initially processed
with a standard convolutional layer to expand the feature dimensions. The
resulting feature maps then go through the downsampling path of the model (the
encoder) consisting of five sequences of dense blocks, residual squeeze-and-
excitation layers and transition-down layers based on max-pooling. In the
encoder, the feature maps at the output of each residual squeeze-and-
excitation layer are concatenated with the input features of the preceding
dense block, in order to encourage feature reuse and improve their
generalizability. At the end of the downsampling path, the _bottleneck_ of the
model consists of a dense block followed by a convolutional LSTM. The
following upsampling path is symmetric to the downsampling one, but it
features: 1) skip connections from the downsampling path for concatenating
feature maps at the corresponding layers of the upsampling path; 2)
transition-up layers implemented through transposed convolutions. Finally, a
convolutional layer provides a 6-channel segmentation map, representing,
respectively, the log-likelihoods of the lobes (5 channels, one for each lobe)
and non-lung (1 channel) pixels.

In the following, we review the novel characteristics of the proposed
architecture.

Residual squeeze-and-excitation layers. Explicitly modeling interdependencies
between feature channels has demonstrated to enhance performance of deep
architectures; squeeze-and-excitation layers [35] instead aim to select
informative features and to suppress the less useful ones. In particular, a
set of input features of size $C\times H\times W$ is squeezed through average-
pooling to a $C\times 1\times 1$ vector, representing global feature
statistics. The “excitation” operator is a fully-connected non-linear layer
that translates the squeezed vector into channel-specific weights that are
applied to the corresponding input feature maps.

Convolutional LSTM. We adopt a recurrent architecture to process the output of
the bottleneck layer, in order to exploit the spatial axial correlation
between subsequent slices and enhance the final segmentation by integrating 3D
information in the model. Convolutional LSTMs [34] are commonly used to
capture spatio-temporal correlations in visual data (for example, in videos),
by extending traditional LSTMs using convolutions in both the input-to-state
and the state-to-state transitions. Employing recurrent convolutional layers
allows the model to take into account the context of the currently-processed
slice, while keeping the sequentiality and without the need to process the
entire set of slices in a single step through channel-wise concatenation,
which increases feature sizes and loses information on axial distance.

Fig. 2 shows an example of automated lung and lobe segmentation from a CT scan
by employing the proposed segmentation network. The proposed segmentation
network is first executed on the whole CT scan for segmenting only lung (and
lobes); the segmented CT scan is then passed to the downstream classification
modules for COVID-19 identification and lesion categorization.

Figure 2: Example of lung and lobes segmentation.

###  3.2 Automated COVID-19 Diagnosis: CT classification

Figure 3: Overview of the COVID-19 detection approach for CT scan
classification as either COVID-19 positive or negative.

After parenchima lung segmentation (through the segmentation model presented
in 2) a deep classification model analyzes slice by slice, each segmented CT
scan, and decides whether a single slice contains some evidence of the
COVID-19 disease. Afterwards, a voting method provides its final prediction
according to all the per-slice decisions. At this stage, the system does not
carry out any identification and localization of COVID-19 lesions, but it just
identifies all slices where patterns of interest may be found and according to
them, makes a guess on the presence or not of COVID-19 induced infection. An
overview of this model is shown in Fig. 3: first the segmentation network,
described in the previous section, identifies lung areas from CT scan, then a
deep classifier (a DenseNet model in the 201 configuration [32]) processes the
segmented lung areas to identify if the slice shows signs of COVID-19 virus.

Figure 4: The DenseNet architecture. Convolutional processing layers are
grouped in Dense Blocks (top). Features extracted in previous layers are
concatenated and fed to all the next layers in the same Dense Block ensuring
maximum information flow. Given that feature maps from previous layers are
passed to the next layers, redundancy is avoided (i.e., later layers do not
need to learn almost identical information from the immediately previous
ones). In this way, each successive layer adds only a small number of feature
maps, the so called growth factor, thus requiring fewer parameters to achieve
state-of-the-art performance. Multiple Dense Blocks can be concatenated and
form a deeper network (bottom).

Once the COVID-19 identification model is trained, we attempt to understand
what features it employs to discriminate between positive and negative cases.
Thus, to interpret the decisions made by the trained model we compute class-
discriminative localization maps that attempt to provide visual explanations
of the most significant input features for each class. To accomplish this we
employ GradCAM [36] combined to VarGrad [37]. More specifically, GradCAM is a
technique to produce such interpretability maps through by investigating
output gradient with respect to feature map activations. More specifically,
GradCAM generates class-discriminative localization map for any class $c$ by
first computing the gradient of the score for class $c$, $s^{c}$, w.r.t
feature activation maps $A_{k}$ of a given convolutional layer. Such gradients
are then global-average-pooled to obtain the activation importance weights
$w$, i.e.:

$w^{c}_{k}=\sum_{i}\sum_{j}\frac{\partial y^{c}}{\partial A^{k}_{ij}}$ (1)

Afterwards, the saliency map $S^{c}$, that provides an overview of the
activation importance for the class $c$, is computed through a weighted
combination of activation maps, i.e.:

$S^{c}=ReLU\left(\sum_{k}w_{k}^{c}A^{k}\right)$ (2)

VarGrad is a technique used in combination to GradGAM and consists in
performing multiple activation map estimates by adding, each time, Gaussian
noise to the input data and then aggregating the estimates by computing the
variance of the set.

###  3.3 COVID-19 lesion identification and categorization

An additional deep network activates only if the previous system identifies a
COVID-19 positive CT scan. In that case, it works on the subset of slices
identified as COVID-19 positives by the first AI system with the goal to
localize and identify specific lesions (consolidation, crazy paving and ground
glass). More specifically, the lesion identification system works on segmented
lobes to seek COVID-19 specific patterns. The subsystem for lesion
categorization employs the knowledge already learned by the COVID-19 detection
module (shown in Fig. 3) and refines it for specific lesion categorization. An
overview of the whole system is given in Fig. 5.

Figure 5: Overview of COVID-19 lesion categorization approach.

###  3.4 A Web-based Interface for Explaining AI decisions to Radiologists

In order to explain to radiologists, the decisions made by a “black-box” AI
system, we integrated the inference pipeline for COVID-19 detection into a
web-based application. The application was designed to streamline the whole
inference process with just a few clicks and visualize the results with a
variable grade of detail (Fig. 6).

Figure 6: The main page of the AI-empowered web GUI for explainable AI. The
user is presented with a list of the CT scan classifications reporting the
models’ prediction.

If the radiologists desire to see which CT slices were classified as positive
or negative, they can click on “Show slices” where a detailed list of slices
and their categorization is showed (Fig. 7).

Figure 7: The summarized classification result showing the CT slices that the
neural network classified as positive or negative.

Because the models may not achieve perfect accuracy, a single slice inspection
screen is provided, where radiologists can inspect more closely the result of
the classification. It also features a restricted set of image manipulation
tools (move, contrast, zoom) for aiding the user to make a correct diagnosis
(Fig. 8).

Figure 8: The slice inspection screen. In this screen the user can inspect
each single slice and the AI models’ decisions.

The AI-empowered web system integrates also a relevance feedback mechanism
where radiologists can correct the predicted outputs, and the AI module
exploits such a feedback to improve its future assessments. Indeed, both at
the CT scan level and at the CT slice level, radiologists can correct models’
prediction. The AI methods will then use the correct labels to enhance their
future assessments.

##  4 Results and Discussion

###  4.1 Dataset

Our dataset contains 72 CT scans of COVID-19 positive patients (positivity
confirmed both by a molecular - reverse transcriptase–polymerase chain
reaction for SARS-coronavirus RNA from nasopharyngeal aspirates - and an IgG
or IgM antibody test) and 94 CT scans of COVID-19 negative subjects (35
patients with interstitial pneumonia but tested negative to COVID-19 and 59
controls).

CT scans were performed on a multi-detector row helical CT system scanner
161616Bright Speed, General Electric Medical Systems, Milwaukee, WI using 120
kV pp, 250 mA, pitch of 1.375, gantry rotation time of 0,6 s and time of scan
5,7 s. The non-contrast scans were reconstructed with slice thicknesses of
0.625 mm and spacing of 0.625 mm with high-resolution lung algorithm. The
images obtained on lung (window width, 1,000–1,500 H; level, –700 H) and
mediastinal (window width, 350 H; level, 35–40 H) settings were reviewed on a
picture archiving and communication system workstation171717Impax ver.
6.6.0.145, AGFA Gevaert SpA, Mortsel, Belgium. CT scans of positive patients
were also annotated by three expert radiologists (through consensus) who
selected a subset of slices and annotated them with the type (Consolidation,
Ground Glass and Crazy Paving) and the location (combinations of
left/right/central and posterior/anterior) of the lesion. In total about 2,400
slices were annotated with COVID-19 lesions and about 3,000 slices of negative
patients with no lesion. Tab. 1 provides an overview of all the CT scans and
annotations in our dataset.

For training the lung/lobe segmentation model we adopted a combination of the
LIDC [38], LTRC181818https://ltrcpublic.com/ and [39] datasets, for a total of
300 CT scans. Annotations on lung/lobe areas were done manually by three
expert radiologists.

Dataset statistics
---
CT Data
CT Scans |  | 166
| COVID-19+ | 72
| COVID-19- | 94
Annotations
Positive slices |  | 2,390
| Ground Glass | 1,035
| Crazy Paving | 757
| Consolidation | 598
Negative slices |  | 2,988
Table 1: CT Dataset for training and testing of the AI models.

###  4.2 Training Procedure

The COVID-19 detection network is a DenseNet201, which was used pretrained on
the ImageNet dataset [40]. The original classification layers in DenseNet201
were replaced by a 2-output linear layer for the COVID-19 positive/negative
classification. Among the set of 166 CT scans, we used 95 scans (36 positives
and 59 negatives) for training, 9 scans for validation (5 positives and 4
negatives) and 62 scans (31 positives and 31 negatives) for test. To compare
the AI performance to the human one, the test set of 62 CT scans was provided
to three expert radiologists for blind evaluation. Given the class imbalance
in the training set, we used the weighted binary cross-entropy (defined in 3)
as training loss and RT-PCR virology test as training/test labels.

The weighted binary cross-entropy loss for a sample classified as $x$ with
target label $y$ is then calculated as:

$WBCE=-w\left[y\cdot\log x+(1-y)\cdot\log(1-x)\right]$ (3)

where $w$ is defined as the ratio of the number negative samples to the total
number of samples if the label is positive and vice versa. This way the loss
results higher when misclassifying a sample that belongs to the less frequent
class. It is important to highlight that splitting refers to the entire CT
scan and not to the single slices: we made sure that full CT scans were not
assigned in different splits to avoid any bias in the performance analysis.
This is to avoid the deep models overfit the data by learning spurious
information from each CT scan, thus invalidating the training procedure, thus
enforcing robustness to the whole approach. Moreover, for the COVID-19
detection task, we operate at the CT level by processing and categorizing each
single slice. To make a decision for the whole scan, we perform voting: if 10%
of total slices is marked as positive then the whole exam is considered as a
COVID-19 positive, otherwise as COVID-19 negative. The choice of the voting
threshold was done empirically to maximize training performance.

The lesion categorization deep network is also a DenseNet201 model where
classification layers were replaced by a 4-output linear layer (ground glass,
consolidation, crazy paving, negative). The lesion categorization model
processes lobe segments (extracted by our segmentation model) with the goal to
identify specific lesions. Our dataset contains 2,488 annotated slices; in
each slice multiple lesion annotations with relative location (in lobes) are
available. Thus, after segmenting lobes from these images we obtained 5,264
lobe images. We did the same on CT slices of negative patients (among the
2,950 available as shown in Tab. 1) and selected 5,264 lobe images without
lesions. Thus, in total, the the entire set consisted of 10,528 images. We
also discarded the images for which lobe segmentation produced small regions
indicating a failure in the segmentation process. We used a fixed test split
consisting of 195 images with consolidation, 354 with crazy paving, 314 with
ground glass and 800 images with no lesion. The remaining images were split
into training and validation sets with the ratio 80/20. Given the class
imbalance in the training set, we employed weighted cross-entropy as training
loss.

The weighted cross-entropy loss for a sample classified as $x$ with target
label $y$ is calculated as:

$WCE=-w\sum^{C}y\cdot log(x)$ (4)

where $C$ is the set of all classes. The weight $w$ for each class $c$ is
defined as:

$w_{c}=\frac{N-Nc}{N}$ (5)

where $N$ is the total number of samples and $N_{c}$ is the number of samples
that have label $c$.

Since the model is the same as the COVID identification network, i.e.,
DenseNet201, we started from the network trained on the COVID-identification
task and fine-tune it on the categorization task to limit overfitting given
the small scale of our dataset.

For both the detection network and the lesion categorization network, we used
the following hyperparameters: batch-size = 12, learning rate = 1e-04, ADAM
back-propagation optimizer with beta values 0.9 and 0.999, eps = 1e-08 and
weight decay = 0 and the back-propagation method was used to update the
models’ parameters during training. Detection and categorization networks were
trained for 20 epochs. In both cases, performance are reported at the highest
validation accuracy.

For lung/lobe segmentation, input images were normalized to zero mean and
unitary standard deviation, with statistics computed on the employed dataset.
In all the experiments for our segmentation model, input size was set to
$224\times 224$, initial learning rate to 0.0001, weight decay to 0.0001 and
batch size to 2, with RMSProp as optimizer. When C-LSTMs were employed,
recurrent states were initialized to zero and the size of the input sequences
to the C-LSTM layers was set to 3. Each training was carried out for 50
epochs.

###  4.3 Performance Evaluation

In this section report the performance of the proposed model for lung/lobe
segmentation, COVID-19 identification and lesion categorization.

####  4.3.1 Lobe segmentation

Our segmentation model is based on the Tiramisu model [31] with the
introduction of _squeeze-and-excitation_ blocks and of a convolutional LSTM
(either unidirectional or bidirectional) after the bottleneck layer. In order
to understand the contribution of each module, we first performed ablation
studies by testing the segmentation performance of our model using different
architecture configurations:

  * 1.

Baseline: the vanilla Tiramisu model described in [31];

  * 2.

Res-SE: residual _squeeze-and-Excitation_ module are integrated in each dense
block of the Tiramisu architecture;

  * 3.

C-LSTM: a unidirectional convolutional LSTM is added after the bottleneck
layer of the Tiramisu architecture;

  * 4.

Res-SE + C-LSTM: variant of the Tiramisu architecture that includes both
residual _squeeze-and-Excitation_ at each dense layer and a unidirectional
convolutional LSTM after the bottleneck layer.

We also compared the performance against the U-Net architecture proposed in
[39] that is largely adopted for lung/lobe segmentation.

All architectures were trained for 50 epochs by splitting the employed lung
datasets into a training, validation and test splits using the 70/10/20 rule.
Results in terms of Dice score coefficient (DSC) are given in Tab. 2. It has
to noted that unlike [39], we computed DSC on all frames, not only on the lung
slices.
The highest performance is obtained with the Res-SE + C-LSTM configuration,
i.e., when adding _squeeze-and-excitation_ and the unidirectional C-LSTM at
the bottleneck layer of the Tiramisu architecture. This results in an accuracy
improvement of over 4 percent points over the baseline. In particular, adding
_squeeze-and-excitation_ leads to a 2 percent point improvement over the
baseline. Segmentation results are computed using data augmentation obtained
by applying random affine transformations (rotation, translation, scaling and
shearing) to input images. The segmentation network is then applied to our
COVID-19 dataset for prior segmentation without any additional fine-tuning to
demonstrate also its generalization capabilities.

Model | Lung segmentation | Lobe segmentation
---|---|---
Baseline Tiramisu [31] | $89.41\pm 0.45$ | 77.97 $\pm$ 0.31
Baseline + Res-SE | $91.78\pm 0.52$ | 80.12 $\pm$ 0.28
Baseline + C-LSTM | $91.49\pm 0.57$ | 79.47 $\pm$ 0.38
Baseline + Res-SE + C-LSTM | 94.01 $\pm$ 0.52 | 83.05 $\pm$ 0.27
Table 2: Ablation studies of our segmentation network in terms of dice score.
Best results are shown in bold. Note: we did not compute confidence intervals
on these scores as they are obtained from a very large set of CT pixels.

####  4.3.2 COVID-19 assessment

We compute results both for COVID-19 detection and lesion categorization and
compare to those yielded by three experts with different degree of expertise:

  1. 1.

Radiologist 1: a physician expert in thoracic radiology ($\sim$30 years of
experience) with over 30,000 examined CT scans;

  2. 2.

Radiologist 2: a physician expert in thoracic radiology ($\sim$10 years of
experience) with over 9,000 examined CT scans;

  3. 3.

Radiologist 3: a resident student in thoracic radiologist ($\sim$3 years of
experience) with about 2,000 examined CT scans.

We also assess the role of prior segmentation on the performance. This means
that in the pipelines showed in Figures 3 and 5 we removed the segmentation
modules and performed classification using the whole CT slices using also
information outside the lung areas. Results for COVID-19 detection are
measured in terms of sensitivity and specificity and given in Tables 3 and 4.

| Sensitivity | C.I. (95%)
---|---|---
Radiologist 1 | 83.9% | [71.8% – 91.9%]
Radiologist 2 | 87.1% | [75.6% – 94.3%]
Radiologist 3 | 80.6% | [68.2% – 89.5%]
AI Model without lung segmentation | 83.9% | [71.8% – 91.9%]
AI Model with lung segmentation |  90.3% | [79.5% – 96.5%]
Table 3: Sensitivity (together with 95% confidence interval) comparison
between manual readings of expert radiologists and the AI model for COVID-19
detection without lung segmentation and AI model with segmentation.

Thus, the AI model using lung segmentation achieves the best performance
outperforming expert radiologists in the COVID-19 assessment. Furthermore,
performing lung segmentation improves by about 6 percent points both the
sensitivity and the specificity, demonstrating its effectiveness. The
important aspect to highlight is that expert radiologists during the
annotation process did not have to segment lungs or lobes, showing the
generalization capabilities of the proposed deep learning-based methods.

| Specificity | C.I. (95%)
---|---|---
Radiologist 1 | 87.1% | [75.6% – 94.3%]
Radiologist 2 | 87.1% | [75.6% – 94.3%]
Radiologist 3 | 90.3% | [79.5% – 96.5%]
AI Model without lung segmentation | 87.1% | [75.6% – 94.3%]
AI Model with lung segmentation |  93.5% | [83.5% – 98.5%]
Table 4: Specificity (together with 95% confidence interval) comparison
between manual readings of expert radiologists and the AI model for COVID-19
detection without lung segmentation and AI model with segmentation.

As a backbone model for COVID-19 identification, we employed DenseNet201 since
it yielded the best performance when compared to other state of the art
models, as shown in Table 5. In all the tested cases, we used upstream
segmentation through the model described in Sect. 2. Voting threshold was set
to 10% on all cases.

Model | Variant | Sensitivity (CI) | Specificity (CI) | Accuracy (CI)
---|---|---|---|---
AlexNet | – | 71.0% (57.9–81.6) | 90.3% (79.5–96.5) | 80.7% (68.3–89.5)
ResNet | 18 | 71.0% (57.9–81.6) | 93.5% (83.5–98.5) | 82.3% (70.1–90.7)
34 | 80.7% (68.3–89.5) | 90.3% (79.5–96.5) | 85.5% (73.7–93.1)
50 | 83.9% (71.9–91.9) | 90.3% (79.5–96.5) | 87.1% (75.6–94.3)
101 | 77.4% (64.7–89.9) | 87.1% (75.6–94.3) | 82.3% (70.1–90.7)
| 152 | 77.4% (64.7–89.9) | 90.3% (79.5–96.5) | 83.9% (71.9–91.9)
DenseNet | 121 | 77.4% (64.7–89.9) | 93.5% (83.5–98.5) | 85.5% (73.7–93.1)
169 | 67.9% (83.5–98.5) | 93.5% (83.5–98.5) | 81.4% (68.7–90.2)
| 201 | 90.3% (79.5–96.5) | 93.5% (83.5–98.5) | 91.9% (81.5–97.5)
SqueezeNet | – | 66.7% (54.5–78.9) | 93.5% (83.5–98.5) | 81.4% (68.7–90.2)
ResNeXt | – | 77.4% (64.7–86.9) | 90.3% (79.5–96.5) | 83.9% (71.9–91.9)
Table 5: COVID-19 classification accuracy by several state of the art models.
Values in parentheses indicate 95% confidence intervals (CI).

In order to enhance trust in the devised AI models, we analyzed what features
these methods employ for making the COVID-19 diagnosis decision. This is done
by investigating which artificial neurons fire the most, and then projecting
this information to the input images. To accomplish this we combined GradCAM
[36] with VarGrad [37]191919https://captum.ai/ and, Fig. 9 shows some examples
of the saliency maps generated by interpreting the proposed AI COVID-19
classification network. It is interesting to note that the most significant
activation areas correspond to the three most common lesion types, i.e.,
ground glass, consolidation and crazy paving. This is remarkable as the model
has indeed learned the COVID-19 peculiar patterns without any information on
the type of lesions (to this end, we recall that for COVID-19 identification
we only provide, at training times, the labels “positive” or “negative”, while
no information on the type of lesions is given).

Figure 9: Lung salient areas identified automatically by the AI model for CT
COVID-19 identification.

For COVID-19 lesion categorization we used mean (and per-class) classification
accuracy over all lesion types and per lesion that are provided, respectively,
in Table 6.

| Model no_segm | Model w_segm
---|---|---
Consolidation | 77.8% (69.9–84.1) | 97.9% (93.6–99.8)
Ground glass | 18.6% (14.1–24.1) | 41.3% (35.1–47.7)
Crazy Paving | 57.1% (49.4–64.4) | 98.3% (94.8–99.8)
Negative | 99.3% (98.6–99.7) | 99.9% (99.5–100)
Average | 63.2% | 84.4%
Table 6: Per-class accuracy for lesion categorization between manual readings
of expert radiologists and the AI model without lung segmentation and AI model
with segmentation. Values in parentheses indicate 95% confidence intervals
(CI).

Mean lesion categorization accuracy reaches, when operating at the lobe level,
about 84% of performance. The lowest performance is obtained on ground glass,
because ground glass opacities are specific CT findings that can appear also
in normal patients with respiratory artifact. Operating at the level of single
lobes yields a performance enhancement of over 21 percent points, and, also in
this case, radiologists did not have to perform any lobe segmentation
annotation, reducing significantly their efforts to build AI models. The most
significant improvement when using lobe segmentation w.r.t. no segmentation is
obtained Crazy Paving class, i.e., 98.3% against 57.1%.

Despite the CT diagnosis of COVID-19 pneumonia seems an easy task for
experienced radiologists, the results show that our system is able to
outperform them providing more accurate decisions. Artificial intelligence
(AI), in particular, is able to identify more accurately lung lesions, in
particular the smaller and undefined ones (as those highlighted in Fig. 9) The
identification elements increases the sensitivity and specificity of the
method for the correct diagnosis. The results obtained both for COVID-19
identification and lesion categorization pave the way to further improvement
by implementing an advanced COVID-19 CT/RX image-driven diagnostic pipeline
interpretable and strongly robust to provide not only the diseases
identification and differential diagnosis but also the risk of disease
progression.

##  5 Conclusions

In this work we have presented an AI-based pipeline for automated lung
segmentation, COVID-19 detection and COVID-19 lesion categorization from CT
scans. Results showed a sensitivity of 90% and a specificity of 93.5% for
COVID-19 detection and average lesion categorization accuracy of about 64%.
Results also show that a significant role is played by prior lung and lobe
segmentation that allowed us to enhance performance of about 6 percent points.
The AI models are then integrated into an user-friendly GUI to support AI
explainability for radiologists, which is publicly available at
http://perceivelab.com/covid-ai. To the best of our knowledge, this is the
first AI-based software, publicly available, that attempts to explain
radiologists what information is used by AI methods for making decision and
that involve proactively in the loop to further improve the COVID-19
understanding. These results pave the way to further improvement to provide
not only the diseases identification and differential diagnosis but also the
risk of disease progression.

## Acknowledgment

We thank the “Covid 19 study group” from Spallanzani Hospital (Maria
Alessandra Abbonizio, Chiara Agrati, Fabrizio Albarello, Gioia Amadei,
Alessandra Amendola, Mario Antonini, Raffaella Barbaro, Barbara Bartolini,
Martina Benigni, Nazario Bevilacqua, Licia Bordi, Veronica Bordoni, Marta
Branca, Paolo Campioni, Maria Rosaria Capobianchi, Cinzia Caporale, Ilaria
Caravella, Fabrizio Carletti, Concetta Castilletti, Roberta Chiappini, Carmine
Ciaralli, Francesca Colavita, Angela Corpolongo, Massimo Cristofaro, Salvatore
Curiale, Alessandra D’Abramo, Cristina Dantimi, Alessia De Angelis, Giada De
Angelis, Rachele Di Lorenzo, Federica Di Stefano, Federica Ferraro, Lorena
Fiorentini, Andrea Frustaci, Paola Gallì, Gabriele Garotto, Maria Letizia
Giancola, Filippo Giansante, Emanuela Giombini, Maria Cristina Greci, Giuseppe
Ippolito, Eleonora Lalle, Simone Lanini, Daniele Lapa, Luciana Lepore, Andrea
Lucia, Franco Lufrani, Manuela Macchione, Alessandra Marani, Luisa Marchioni,
Andrea Mariano, Maria Cristina Marini, Micaela Maritti, Giulia Matusali,
Silvia Meschi, Francesco Messina Chiara Montaldo, Silvia Murachelli, Emanuele
Nicastri, Roberto Noto, Claudia Palazzolo, Emanuele Pallini, Virgilio Passeri,
Federico Pelliccioni, Antonella Petrecchia, Ada Petrone, Nicola Petrosillo,
Elisa Pianura, Maria Pisciotta, Silvia Pittalis, Costanza Proietti, Vincenzo
Puro, Gabriele Rinonapoli, Martina Rueca, Alessandra Sacchi, Francesco Sanasi,
Carmen Santagata, Silvana Scarcia, Vincenzo Schininà, Paola Scognamiglio,
Laura Scorzolini, Giulia Stazi, Francesco Vaia, Francesco Vairo, Maria
Beatrice Valli) for the technical discussion and critical reading of this
manuscript.

## Regulation and Informed Consent

All data and methods were carried out in accordance to the General Data
Protection Regulation 2016/679. The experimental protocols were approved by
the Ethics Committee of the National Institute for Infectious Diseases Lazzaro
Spallanzani in Rome. All patients enrolled in the study were over 18 at the
time of their participation in the experiment and signed informed consent.

## Declarations of interest

None.

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