footnote 11institutetext: Warsaw University of Technology, Warsaw, Poland
22institutetext: Sano Centre for Computational Medicine, Cracow, Poland
33institutetext: Jagiellonian University, Cracow, Poland 44institutetext:
Tooploox, Wroclaw, Poland

# POTHER: Patch-Voted Deep Learning-based Chest X-ray Bias Analysis for
COVID-19 Detection

Tomasz Szczepański 11 0000-0001-6189-478X Arkadiusz Sitek 22
0000-0002-0677-4002 Tomasz Trzciński 11 3 3 4 4 0000-0002-1486-8906 Szymon
Płotka 11 2 2 0000-0001-9411-820X

###### Abstract

A critical step in the fight against COVID-19, which continues to have a
catastrophic impact on peoples lives, is the effective screening of patients
presented in the clinics with severe COVID-19 symptoms. Chest radiography is
one of the promising screening approaches. Many studies reported detecting
COVID-19 in chest X-rays accurately using deep learning. A serious limitation
of many published approaches is insufficient attention paid to explaining
decisions made by deep learning models. Using explainable artificial
intelligence methods, we demonstrate that model decisions may rely on
confounding factors rather than medical pathology. After an analysis of
potential confounding factors found on chest X-ray images, we propose a novel
method to minimise their negative impact. We show that our proposed method is
more robust than previous attempts to counter confounding factors such as ECG
leads in chest X-rays that often influence model classification decisions. In
addition to being robust, our method achieves results comparable to the state-
of-the-art. The source code and pre-trained weights are publicly available at
(https://github.com/tomek1911/POTHER).

###### Keywords:

COVID-19 Deep learning Explainable AI

##  1 Introduction

The SARS-CoV-2 outbreak has claimed the lives of millions of people, and
despite its onset in 2019, it remains a serious concern and a threat to public
health. The gold standard for diagnosing COVID-19 disease is an RT-PCR test;
however, it is expensive, necessitates specialised laboratories and requires
the patient to wait relatively long for the outcome. For this reason, computer
scientists and radiologists become interested in the computer-aided diagnosis
(CAD) capabilities of chest X-rays (CXR). The automatic diagnosis of COVID-19
using chest X-ray images is challenging due to the high intra-class
variations, superimposition of anatomical structures, or implanted electronic
devices [1]. Moreover, a significant limitation in developing reliable models
for detecting pneumonia and COVID-19 is the lack of precisely annotated and
rigorously collected data. These limitations may result in models learning
various confounding factors in the CXRs. We investigate this phenomenon in our
work.
Most of the approaches developed for the classification of the CXR images
relay on global features [2], [11], [12], [14]. However, these features may
not accurately represent the complex nature of CXR images [22]. It should be
verified when working with medical images, especially when the model’s
accuracy is very high [13], [21], the reasons why algorithms perform so well
to prevent developing algorithms which base their decision on confounding
factors rather then medical pathology. Wang et al. [7] propose COVID-Net, the
first lightweight capacity neural network dedicated for COVID-19 detection and
introduce a novel COVIDx dataset. Authors create a feature extraction method
tailored for COVID-19 and provide results of the model’s decisions analysis
with the use of GSInquire [24]. They create a map of lung areas important for
COVID-19 detection and claim a production-ready solution. Considering the need
for a critical approach to chest classification and analysis of
biases[23],[22],[20], we carry out further research into the analysis of the
deep learning model decisions using the COVIDx dataset.
Recently, a new patch-based learning technique [3], [10], [25] emerged as a
successful method for robust model learning and generalization. Li et al. [3]
propose a multi-resolution patch-based CNN approach for lung nodule detection
on CXR image. The method achieves high accuracy by leveraging the local
context of CXR images. Roy et al. [25] use the model’s local training method
for classification on the ICIAR-2018 breast cancer histology image dataset,
which allows them to achieve state-of-the-art results on this dataset. Oh et
al. [10], inspired by their statistical analysis of the potential imaging
biomarkers of the CXRs, explore patch-based learning for COVID-19 detection.
They propose random patch cropping, from which the final classification result
is obtained by majority voting from inference results at multiple patch
locations. Authors analyse model decisions using proposed probability
gradient-weighted class activation map (GradCAM) [18] as a part of explainable
AI (XAI) method [4], upon which they conclude their results are correlated
with radiological findings. Similar to Oh et al., our proposed method POTHER
explores a patch-based learning approach in CXR images for reliably detecting
COVID-19. In contrast, we do not use segmented lungs as input; instead, we
propose a multi-task model that leverages segmentation task to extract
valuable features. In addition, we limit the area from which we draw patches
and reduce their size.
We use XAI methods to demonstrate that model decisions may rely on confounding
factors rather than medical pathology. Degrave et al. [20] and Cabrera et al.
[22] call them shortcuts, while Maguolo et al. [23] biases. An analysis of
shortcuts in CXRs, which we call confounding biases (CBs), based on open-
source datasets and global learning methods, is presented by [20]. It
demonstrates the detrimental influence on models decisions caused by
laterality tokens, i.e. L or R letter, meaning the left or right side of the
image, the position of the clavicles and the presence of arms in the upper
parts of the image. Authors present that machine learning (ML) models trained
on CXR images may generalise poorly and owe the majority of their performance
to the learning of shortcuts that may be consistently detected in both
internal and external domains - making external validation alone insufficient
to detect poorly behaved models. We analyse CBs in the frequently cited COVIDx
dataset [7] and propose a multi-task learning COVID-19 detection method called
POTHER that improves robustness to CBs.

Figure 1: The overview of the proposed POTHER framework for chest X-ray bias
analysis. (a) Pre-trained segmentation network to generate pseudo-labels, (b)
an encoder-decoder patch-based multi-task learning network to classify CXR
images. We adopt majority patch-based voting for classification and employ
patch-based activation maps to explain the results.

The contributions of this paper are threefold. Firstly, we propose a novel
multi-task patch-voted learning-based method called POTHER for chest X-ray
image analysis for COVID-19 detection. Secondly, we analyse activation maps of
currently available methods and sources of confounding biases in CXRs from the
COVIDx dataset. Our analysis reveals a significant number of CBs. These biases
should be considered when using the COVIDx dataset in future research. The
main CBs are ECG leads, laterality tokens and hospital markings. Finally, we
demonstrate that deep learning models learn how to classify pneumonia
manifestations and lung morphology, focusing on the confounding mentioned
above factors rather than the actual manifestation of the disease or lack
thereof on lung parenchyma. In our methods, to counter CBs, we used
segmentation as a helper task which allowed for an efficient feature
extraction method that performs comparably to the state of the art. The paper
is organized as follows. Section 2 details our proposed method. Next, Section
3 presents details about our experimental setup and results. We follow with
Section 4 which discusses the results. Finally, Section 5 concludes the paper.

##  2 Method

Fig. 1 presents an overview of our proposed method. First, we pre-train the
U-Net lung fields segmentation network to generate pseudo-labels. Second, we
use patches to train a multi-task U-Net-based neural network for CXR image
bias analysis. Finally, for classification, we use a majority patch-based
voting.
Pre-training segmentation network. We train a segmentation neural network to
extract lung fields from CXR images. For this task, we adopt the U-Net
encoder-decoder to perform semantic segmentation [5]. With the pre-trained
model on [8], [9] datasets, we generate pseudo labels for unlabelled COVIDx
dataset [7]. Before feeding those pseudo masks to the multi-task neural
network, we use custom pre-processing algorithms.
Multi-task neural network. Inspired by [15], [16], [17], we use an encoder-
decoder based convolutional neural network (CNN) for simultaneous
classification and segmentation of the lungs in the CXRs. Our network is
U-Net-based with ImageNet pre-trained ResNet-50 as backbone encoder [6]. We
use an encoder part to extract high-level X-ray image features. Every encoder
block forwards feature maps and concatenates them with the corresponding
decoder part. We employ an attention mechanism to the skip connections to
learn salient maps suppressing irrelevant lung vicinity that may be a source
of CBs. We extend an attention gate mechanism by aggregating multi-scale
feature maps from the decoder to learn the local context of the lung feature
maps representation. Each feature map is fed through an inception module that
leverages convolutional filters of multiple kernels (i.e. 1x1, 3x3, 5x5, and
7x7) and stride sizes ($S=1,S=2$), which does not increase the number of
parameters significantly. The amplitudes of features from the deeper layers
are smaller than the shallow ones. To prevent shallow layers from dominating
deeper ones, we normalise the weights of the features from multiple scales
with the L2 norm before concatenation.
Patch-based learning. Motivated by [10], we adopt a patch-based learning
method to train our multi-task neural network. Unlike Oh, we do not cut out
the lungs with masks to avoid inductive bias. We use the lung masks instead as
pseudo-labels for segmentation training. This helper task requires the model
to learn new features necessary to recognise lung tissue boundaries. A pre-
processed CXR image and corresponding mask are resized to $1024\times 1024$.
Then using a draw area based on the scaled-down, with a ratio of 0.9, whole
lung mask, we randomly choose a patch centre. Its coordinates are drawn with a
uniform distribution of non-zero pixels from the draw area mask. Fig. 1(b)
shows training images with corresponding pseudo-labels in the context of the
entire CXR image, i.e. exemplary drawn image patches are marked with white
squares in the CXR image and pseudo-labels with red squares on the
corresponding mask. In our method, we use patches of size $80\times 80$, in
contrast to Oh uses patches of size $224\times 224$. A region of the image and
the corresponding mask are cut off using determined patch coordinates,
resulting in an image patch and pseudo-label pair. Then the image patch and
its mask are interpolated to $224\times 224$. This pair is used to train a
segmentation head, while the image patch and its corresponding class represent
a training pair for the classification head. Thanks to the reduction of the
draw area and the small size of the patch, the model’s input never contains
laterality tokens and hospital markings. At the same time, if chosen close to
the lung edge, a patch covers a small lung boundary fragment, allowing the
model to recognise lung tissue based on the segmentation task.
Majority patch-based voting. Only one patch per image, per batch, is used in
the training phase. However, we repeat the random draw multiple times for the
inference to cover the whole lung field as the area of interest. Each time,
based on the patch, the model makes a single classification called a single
vote. The majority vote result, i.e. the class chosen based on the majority of
the patches, is the final classification for that image.

##  3 Experiments and Results

In this section, we evaluate our methods on the COVIDx dataset. Next, we
present gradient-weighted class activation maps that compare the decision
bases of our proposed method with existing methods. We employ GradCAM to
provide visual explanations of our method decision bases that focuses its
attention on lung morphology and is less sensitive to CBs. Finally, we show
quantitative results on the COVIDx test set.

###  3.1 Datasets

To pre-train a neural network for the segmentation of lungs, we use data from
[8], [9]. The datasets consist of 6380 2D CXR images. The annotations for each
image provide a manual segmentation mask rendered as polygons, including the
retrocardiac region. To evaluate our methods, we use an open-source COVIDx
dataset [7]. In total, the dataset consists of 13970 2D CXR images (8806
normal, 5551 pneumonia and 353 COVID-19 cases). The authors constantly expand
this dataset, and in order to compare with their published results, we use the
same version of the dataset as they used at the time of publication. We create
a validation set by splitting the training set with a 70:30 ratio, and the
test dataset is built using the script provided by the COVIDx authors.

###  3.2 Data pre-processing

Lung masks. We remove unnecessary objects from lungs masks like electronic
devices, which are labelled with a shade of grey. We perform mask filtering to
improve the masks or remove them from the dataset when the correction is not
possible. We present a detailed algorithm in the supplement
111https://cutt.ly/rIB1JFQ. Finally, we resize the original image and the mask
to a size of $1024\times 1024$ pixels using linear interpolation. All images
are resized to square regardless of their original aspect ratio.

COVIDx dataset. The images for the training of our model are pre-processed
with histogram equalisation. Soft augmentation methods consist of offset,
scaling without preserved aspect ratio, rotation, horizontal flip, Contrast
Limited Adaptive Histogram Equalisation (CLAHE) with random clip range and
random grid range, brightness and contrast adjustment, sharpening and
embossing.

###  3.3 Implementation details

We implement our model in PyTorch deep learning framework and train on a
workstation with a single GPU NVIDIA Titan RTX 24GB until convergence over 100
epochs, with a mini-batch size of 16, an initial learning rate of $1\times
10^{-4}$ and a weight decay factor of $1\times 10^{-4}$. We set Rectified Adam
(RAdam) as the optimiser to minimise the loss function. To prevent
overfitting, we apply various soft data augmentation techniques. During
training, we perform the following transformations: horizontal flip, sharpen,
emboss and CLAHE with $p=0.5$. We also apply a random-weighted sampler. The
weights are computed as an inverse class frequency. As loss function, we use
Dice loss for segmentation task:

$\mathcal{L}_{dice}\ =\
1-\frac{2\sum_{i}^{N}p_{i}g_{i}+\epsilon}{\sum_{i}^{N}p_{i}^{2}+\sum_{i}^{N}g_{i}^{2}+\epsilon},$
(1)

where $p_{i}$ is the prediction pixel value, $g_{i}$ is the ground truth, and
$\epsilon$ is a numerical stability to avoid divide by zero errors, and
Weighted Cross-Entropy (WCE) for classification task:

$\mathcal{L}_{WCE}\ =\
-\frac{1}{N}\sum_{n=1}^{N}wr_{n}\log(p_{n})+(1-r_{n})\log(1-p_{n}),$ (2)

where $w$ is the class weight, $p_{n}$ is the Softmax probability for the
$i^{th}$ class, and $r_{n}$ is the ground truth value of $\\{0,1\\}$. Finally,
the overall multi-task loss function can be formulated as the sum of both loss
functions:

$\mathcal{L}=\mathcal{L}_{Dice}+\mathcal{L}_{WCE}.$ (3)

###  3.4 Gradient-weighted class activation mapping results

We analyse probabilistic class activation maps for gradient weighted models
that classify CXR images into three classes: normal, pneumonia and COVID-19 in
the COVIDx dataset. It turns out that the globally [7] and the locally [10]
trained model focus on non-disease related elements. In contrast, POTHER
focuses on the morphological structure of lung tissue. The globally trained
model concentrates on laterality tokens and ECG-leads artifacts even though
input images are cropped. The locally trained model lacks textual information,
but instead, it uses the lung mask’s contour, supported by an edge’s strong
gradient because of the black background, to classify the image. The globally
trained ResNet-50 model was fed with images pre-processed and augmented, as
described in Wang’s work. The pre-processing consists of cropping the top 8%
of the image, and according to the authors, it is to mitigate commonly-found
embedded textual information, which, as presented in Fig. 2, is not enough
because there are texts localised too close to the lung to be cropped.

|  |
---|---|---
|  |
|  |
a) Wang et al. | b) Oh et al. | c) POTHER
Figure 2: The activation maps of: (a) cropped image global training, (b)
segmented lung patch-based local training, (c) our multi-task patch-based
local training method - POTHER. The globally trained model focuses on
artificial electronics, textual information, and other non-disease-related
features such as shoulder position. The model trained on the segmented lung is
spared textual information but uses lung contour for classification. Our
proposed model does not use these spurious features for classification but
lung morphology instead.

Fig. 3 compares activation maps of two models trained with a patch-based
learning approach. We can see that Oh’s patches cover a considerable
percentage of the image, allowing the model to focus on the lung’s corner or
ECG leads. On the contrary, we train POTHER on the whole unsegmented images.
It does not use pixels outside the lung because of small patches and reduced
mask areas inside which patch centres can be located. Though some of the
POTHER’s patches are confused by cable on the healthy patient’s image, thanks
to their relatively small area, the vast majority of patches vote for a proper
class during majority voting.

|
---|---
|
a) Oh et al. | b) POTHER
Figure 3: Patch learning activation maps comparison: (a) Oh et al. train the
model using big patches which, if placed close to the lung edge, provide a
model with awareness of lung contour, allowing it to learn that, (b) POTHER
model focuses on fine-grained lung markings like, e.g. interstitial opacities.

We analyse activation maps of the POTHER model based on the visible
manifestations of pneumonia and COVID-19 on CXRs. Visible with an untrained
eye, pneumonia and COVID-19 pneumonia signs are related to increased lung
density, which may be seen as whiteness in the lungs, which obscures the lung
markings that are typically seen depending on the severity of pneumonia [19].
During COVID-19, markings are partially obscured by the increased whiteness.
These can be subtle and require a confirmation from a radiologist. COVID-19
pneumonia changes, like lung involvement, are mostly bilateral on chest
radiograph. Areas of lungs - Fig. 4, where increased whiteness can be found
are pointed with blue (pneumonia) and red (COVID-19) arrows, and the region
free of it is marked with green arrows. It can be seen that POTHER’s decisions
about patches are correlated with them, which may suggest a classification
based on the disease symptoms seen on the CXR.

|
---|---
|
a) Classification | b) Pathology area
Figure 4: POTHER activation maps and per-patch classification results - (a)
the model output class is coded with patch bounding box colour: green - normal
lung, blue - pneumonia, red - COVID-19. (b) black arrows point to the area
with white opacity characteristic of COVID-19, white arrows point to lower
lobes opacity characteristic to different types of pneumonia, outlined arrows
point to the area free of opacity. The areas indicated by the arrows and the
POTHER classifications overlap, indicating that the model may consider
manifestations specific to the classified diseases when making decisions.

The locations and neighbourhoods of ECG lead elements and other non-anatomical
artifacts are analysed in more detail. Fig. 5 presents generated activation
maps of the analysed models, strongly indicating that electronic devices’
presence in the image focuses heavily the model’s attention and can be an
important factor in decision-making. Using POTHER, we classify the exact
locations, and its activation maps show no significant focus on artificial
elements. Fig. 5(f) shows that 3 out of 9 POTHER’s patches change their output
near the cable; however, thanks to the majority voting it does not influence
the final decision.

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

(j)

(k)

(l)

Figure 5: Analysis of activation maps in the vicinity of elements influencing
the model decision. Comparison of the models: (b, e) trained globally, (h, k)
segmented-lung patch-based and (c, f, i, l) with our POTHER method. The left
column of figures (a, d, g, j) shows fragments of the original CXR image,
figures (b, e, h, k) a fragment with superimposed activation map and the right
column (c, f, i, l) activation maps of individual POTHER patches and their
color-coded votes.

###  3.5 Quantitative analysis

To evaluate results, we use precision, recall, F1 score and accuracy. As a
test set, we use a COVIDx introduced by Wang et al. [7]. It consists of 300
images equally distributed into three classes. A globally trained model’s
inference results in a single classification per image, while inference of a
locally trained one requires majority voting before making the final decision.
When we train Oh’s patch-based learning model, we follow the same pre-
processing and augmentations as the author and the only differences are the
model used for lungs segmentation and our mask filtration algorithm. We
compare the performance of four models that are trained on the COVIDx dataset.
The results presented in Table 1 are comparable in terms of accuracy, which
oscillates around 90%. However, COVID-Net’s accuracy, as well as its precision
of COVID-19 classification, stands out. Our method achieves the highest F1
score of 0.974 for the COVID-19 class. Oh’s model performs similarly to the
results reported in their work, where it scored an accuracy of 0.889 on their
dataset.

Table 1: The performance comparison on COVIDx test set Method | Class |  | Precision | Recall | F1 |  | Accuracy
---|---|---|---|---|---|---|---
ResNet-50 [7] | Normal |  | 0.882 | 0.970 | 0.924 |  |
| Pneumonia |  | 0.868 | 0.920 | 0.893 |  | 0.906
| COVID-19 |  | 0.988 | 0.830 | 0.902 |  |
COVID-Net [7] | Normal |  | 0.905 | 0.950 | 0.927 |  |
| Pneumonia |  | 0.913 | 0.940 | 0.926 |  | 0.933
| COVID-19 |  | 0.989 | 0.910 | 0.948 |  |
Patch learning [10] | Normal |  | 0.815 | 0.970 | 0.886 |  |
| Pneumonia |  | 0.914 | 0.813 | 0.860 |  | 0.886
| COVID-19 |  | 0.963 | 0.867 | 0.912 |  |
Ours (POTHER) | Normal |  | 0.790 | 0.980 | 0.875 |  |
| Pneumonia |  | 0.963 | 0.780 | 0.862 |  | 0.903
| COVID-19 |  | 1.000 | 0.950 | 0.974 |  |

##  4 Discussion

In this work, we propose a novel learning method specifically designed to
address difficulties inherent in CXR images. In addition, we analyse the
model’s biases when it is making classification decisions. The improvement of
diagnostic capabilities and the reduction of the negative influence of the
numerous CBs in the COVIDx dataset guide us during model development. Many
researchers have achieved very high scores on CXR images when trying to
improve COVID-19 classification, but unfortunately, very few have analysed
what allows the model to achieve such high scores.
When trying to improve COVID-19 classification based on CXRs, great attention
should be paid to understanding what features allow the model to achieve good
classification. Otherwise, we may obtain a model with little diagnostic value,
though it achieves seemingly high scores utilising CBs to classify on dataset
that it was trained and tested. We reveal potential pitfalls in the CXR
medical imaging domain, i.e. some types of clues irrelevant to disease
recognition, like hospital markings and ECG leads, causing the model to miss
the point of the disease symptoms. The list of CBs is not exhaustive as it
cannot be guaranteed that the model will not shift its attention to something
similarly undesired if known CBs are addressed. Therefore, a dataset
specifically designed to eliminate as many detected CBs as possible is
desirable. Annotation or segmentation of the pathological lung area by a
radiologist visible in the CXR would also be valuable. According to [19], a
chest radiograph can look normal in up to 63% of people infected with
COVID-19, especially in the early stages. Using COVID-19 labelled radiograph
with no noticeable disease-related changes in the lung image is very likely
confusing to the model and encourages learning of undesirable confounding
features.

##  5 Conclusions

In this paper, we proposed a novel multi-task patch-voted learning-based
method called POTHER for CXR-image-based COVID-19 detection. Our model learns
pneumonia manifestations and lung morphology features. We performed the
activation map analysis and showed that the deep learning methods are
susceptible to learning features unrelated to the pathology. The COVIDx
dataset contains many images that can confuse the learned model, such as ECG
leads, laterality tokens or other hospital-specific markings. To this end,
POTHER’s patches include the area of lung fields and only its closes vicinity
to eliminate some of the sources of undesirable features. Using segmentation
task with an attention mechanism provides an efficient feature extraction,
allowing satisfactory results despite training with limited lung fragments.
Thanks to training with small patches, our method is less sensitive to the
CBs. In future work, we will apply our method with Vision Transformer (ViT)
[26] on a large-scale CXR dataset to improve the model generalisation with
more complex lung disease classes.

## Acknowledgements

This work is supported in part by the European Union’s Horizon 2020 research
and innovation programme under grant agreement Sano No. 857533 and the
International Research Agendas programme of the Foundation for Polish Science,
co-financed by the EU under the European Regional Development Fund. This
research was funded by Foundation for Polish Science (grant no
POIR.04.04.00-00-14DE/18-00 carried out within the Team-Net program co-
financed by the European Union under the European Regional Development Fund),
National Science Centre, Poland (grant no 2020/39/B/ST6/01511).

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