# Automated detection of celiac disease on duodenal biopsy slides:
a deep learning approach

###### Abstract

Celiac disease prevalence and diagnosis have increased substantially in recent
years. The current gold standard for celiac disease confirmation is visual
examination of duodenal mucosal biopsies. An accurate computer-aided biopsy
analysis system using deep learning can help pathologists diagnose celiac
disease more efficiently. In this study, we trained a deep learning model to
detect celiac disease on duodenal biopsy images. Our model uses a state-of-
the-art residual convolutional neural network to evaluate patches of duodenal
tissue and then aggregates those predictions for whole-slide classification.
We tested the model on an independent set of 212 images and evaluated its
classification results against reference standards established by
pathologists. Our model identified celiac disease, normal tissue, and
nonspecific duodenitis with accuracies of 95.3%, 91.0%, and 89.2%,
respectively. The area under the receiver operating characteristic curve was
greater than 0.95 for all classes. We have developed an automated biopsy
analysis system that achieves high performance in detecting celiac disease on
biopsy slides. Our system can highlight areas of interest and provide
preliminary classification of duodenal biopsies prior to review by
pathologists. This technology has great potential for improving the accuracy
and efficiency of celiac disease diagnosis.

Index Terms—  celiac disease, biopsy, whole-slide imaging, deep learning,
digital pathology

##  1 Introduction

Celiac disease (CD), an autoimmune disorder triggered from the consumption of
gluten, affects as much as one percent of the population worldwide [1, 2].
Patients who are diagnosed with CD undergo treatment in the form of a lifelong
gluten-free diet, which requires substantial patient education, motivation,
and follow-up [3]. Recent studies have found that the prevalence of CD has
increased dramatically in the United States and Europe, and that undiagnosed
CD was associated with a nearly four-fold increase in risk of death [4-6]. In
fact, CD remains undiagnosed in the majority of affected people, highlighting
the need for more frequent and accurate methods for its detection [7-9].

Celiac disease diagnosis involves serological testing of celiac-specific
antibodies, followed by microscopic examination of duodenal biopsies, which
are considered the gold standard in diagnostic confirmation of CD [10, 11].
Typically, four to six duodenal samples are taken from the patient by an
endoscopic procedure, and these samples are visually examined by a
pathologist. A confirmatory diagnosis requires detection of histological
changes associated with the disease, which are classified according to
guidelines from either Marsh [12], Marsh modified (Oberhuber) [13], or Corazza
[14]. Endoscopic findings that indicate CD include scalloped folds with or
without mosaic pattern mucosa, reduction in the number of folds, and nodular
mucosa [15]. The spectrum of histologic changes in CD ranges from only
increasing intraepithelial lymphocytes with preserved villous architecture to
mild villous blunting to complete villous atrophy [16]. Studies have shown
that the histological diagnosis of biopsies are subject to a significant
degree of inter-observer variability [17-20]. One potential method for
improving the accuracy of CD detection on duodenal biopsies is to apply
automated image analysis to aid pathologists. Because the prevalence of CD is
increasing, active case-finding is currently being used to screen more
patients [21, 22]. An automated biopsy analysis system could help pathologists
by filtering and pre-populating scans, improving efficiency and turnaround-
time.

Fig. 1: Data flow diagram for allocating whole slides for training,
development, and testing of our model. For training, patches were generated
using the sliding window algorithm to train our ResNet patch classifier. The
development set was used to fine-tune hyperparameters and thresholds of our
neural network. Finally, we evaluated our model on the test set of 212 whole-
slide images with reference labels.

Recently, a subfield in artificial intelligence known as deep learning has
produced a set of image analysis techniques that automatically extract
relevant features, transforming the field of computer vision [23]. Deep neural
networks use a data-driven approach to learn multi-level representations of
data, allowing for comprehensive image analysis and classification [24]. These
techniques are being increasingly applied to medical imaging to assist
radiologists and pathologists [25]. In gastroenterology, previous studies have
already used deep neural networks to classify colorectal polyps on biopsy and
colonoscopy images [26-28], intraductal papillary mucinous neoplasms in MRI
images [29], and diabetic retinopathy in retinal fundus photographs [30]. For
CD in particular, large video datasets captured during endoscopies have
facilitated quantitative analysis with deep learning [31, 32]. However,
endoscopic classification is for the most part not used for confirming the
diagnosis of CD. In this study, we developed a deep learning model that
detects CD from duodenal biopsy images, the gold standard for diagnosis. We
evaluated our model on an independent test set of 212 whole-slide images.

##  2 METHODS

###  2.1 Data Collection

To train and evaluate our model for celiac disease detection, we collected
whole-slide images from all patients who underwent duodenal biopsies from 2016
to 2018 at the Dartmouth-Hitchcock Medical Center (DHMC), a tertiary academic
care center in Lebanon, NH. These slides contain hematoxylin-eosin stained
formalin-fixed paraffin-embedded (FFPE) tissue specimens and were scanned by a
Leica Aperio whole-slide scanner at 20x magnification by the Department of
Pathology and Laboratory Medicine at DHMC. In total, we collected 1230 slides
from 1048 patients. We randomly partitioned 1018 of these whole-slide images
from 681 patients for model training and 212 whole-slide images from 163
patients as an independent test set for final evaluation of our model. There
was no patient overlap for the slides in the training and test sets.

###  2.2 Slide Annotation

All whole-slide images used in our study were diagnosed by attending
pathologists on gastrointestinal pathology service at the time as either
normal, celiac disease, or nonspecific duodenitis. Normal duodenal biopsies
show preserved villous architecture with no mucosal injury or acute or chronic
inflammation. Celiac disease biopsies show a spectrum of histologic changes as
described in Marsh classification [12], including partial to total villous
atrophy with intraepithelial lymphocytosis, chronic inflammation and crypt
regenerative hyperplasia. Nonspecific duodenitis includes histologic changes
including peptic duodenitis, drug induced injury, and various other
differential diagnoses of villous atrophy and acute and chronic inflammation.
These labels were parsed from the medical record database and assigned as
reference standard for slides used during model training. The training slides
were then further split into a training set of 790 images and a development
set of 228 images. Training set slides were used for training our neural
network, while development set images were used for hyperparameter tuning. For
the independent test set of 212 images, however, all labels were separately
reviewed and confirmed by two gastrointestinal pathologists. Disagreements
between original labels and new labels were reviewed by a senior
gastrointestinal pathologist, who determined final classifications. The class
distributions and roles in the data flow for our training, development, and
test set are shown in Figure 1.

###  2.3 Model Development

In recent years, research in deep learning has demonstrated successful
application of convolutional neural networks for image classification,
including medical image analysis. In our study, we used the deep residual
network (ResNet) [33], a neural network architecture built from residual
blocks. ResNet significantly outperforms early deep learning models such as
AlexNet [34] and VGG [35], and achieved state-of-the-art performance on the
ImageNet and COCO image recognition benchmarks [36, 37]. We implemented ResNet
to take in square patches as inputs and output a prediction probability for
each of the three classes: normal, celiac disease, and nonspecific duodenitis.

For model training, we used a sliding window method on each high-resolution
whole-slide image to generate small patches of size 224 by 224 pixels. Since
some classes had more whole-slide images than others, we generated patches
with different overlapping areas for each class. When inputting a patch into
the model for training, we normalized the RGB color channels to the mean and
standard deviation of the entire training set to neutralize differences in
color among slides. Then, we performed color jittering on the brightness,
contrast, saturation, and hue of each patch. Finally, we randomly rotated and
flipped the images across the horizontal and vertical axes. In total, we
generated 80,000 patches for each of our three classes, which were then
uniquely augmented during each epoch in training.

In terms of model parameters, we initialized ResNet-50, the fastest ResNet
with three-layer residual blocks, with weights from the He initialization
[38]. We trained our ResNet model by optimizing on a multi-class cross-entropy
loss function for forty epochs on the augmented training set, starting with an
initial learning rate of 0.0001 and decaying by a factor of 0.85 every epoch.
We used the Adam optimizer [39] and weight decay regularization (L2 penalty)
[40] of 0.0001. Total training time was twelve hours on a Titan Xp graphics
processing unit (GPU).

###  2.4 Whole-Slide Inference

In whole-slide inference, we aimed to classify each whole-slide image as
either normal, celiac disease, or nonspecific duodenitis. The model is trained
to classify small patches rather than entire slides, so we again used the
sliding window algorithm to break down each whole slide into a collection of
patches, each overlapping by one-third area. Next, we applied our trained
ResNet model to classify each patch, and we filtered out noise using
thresholding to discard predictions of low confidence. Given the distribution
of patch predictions, we used the following heuristic to determine the whole-
slide class: if more than $\gamma$ patches were classified as nonspecific
duodenitis, then the whole slide was classified as nonspecific duodenitis.
Otherwise, the most commonly predicted class was chosen as the whole-slide
prediction. Thresholds for filtering noise, as well as $\gamma$, were
optimized by performing a grid search over the development set. This allowed
for accurate classification of slides with a significant amount of nonspecific
duodenitis that was not covering the majority of the specimen area. Figure 2
depicts the whole-slide inference process. Inference time for a single whole-
slide image was about fifteen seconds on a single Titan Xp GPU.

Fig. 2: Overview of detection of celiac disease on whole-slide biopsy images.
We used a sliding window approach on a whole-slide image to generate patches,
classified each patch with a ResNet model, and used a heuristic on the
aggregated patch predictions to classify the whole slide.

###  2.5 Evaluation and Visualization

For final evaluation, we applied our model to the independent test set of 212
whole-slide images. We compared the predictions of our model with reference
standards established by pathologists, and measured accuracy, precision,
recall, and F1 score for each class. We calculated confidence intervals for
all performance metrics using the Clopper and Pearson method [41]. In
addition, we plotted Receiver Operating Characteristics (ROC) curves and
calculated area under the curve (AUC) for each class.

Furthermore, we visualized our model’s predictions at both the whole-slide and
patch level. At the whole-slide level, we overlaid color-coded dots on patches
for which the model predicted a particular pattern. This helps pathologists
quickly identify regions of the slide containing abnormal tissue. At the patch
level, we used the class activation mapping (CAM) method [42] to generate a
pixel-level heat map that highlights the most informative regions of the image
relevant to the predicted class. This demystified our classification method
for each patch by revealing the most significant histologic features on the
patch for each class for our model.

##  3 RESULTS

For model selection, we validated our neural network model on the development
set of 228 images. We found the optimal thresholds for filtering out noise at
the patch-level to be 0.7 for the normal class, 0.8 for the celiac disease
class, and 0.85 for nonspecific duodenitis. For selection of the $\gamma$
threshold for percent area needed to classify a whole-slide as nonspecific
duodenitis, we considered our gastrointestinal pathologist’s subjective
examination of our model’s predictions in addition to a grid search to arrive
at $\gamma$ = 0.25. After threshold optimization, our best model applied to
the development set achieved an accuracy of 95.6% for normal, 98.7% for celiac
disease, and 94.3% for nonspecific duodenitis after threshold optimization.

Performance of our model on the independent test is shown in Table 1, which
includes accuracy, precision, recall, and F1 score with 95% confidence
intervals. Notably, our model detects the presence of CD with an accuracy of
95.3% and an F1 score of 93.5%. Table 2 shows the confusion matrix for
predicted labels versus reference labels. ROC curves and AUC for each class
are shown in Figure 3. AUC was greater than 0.95 for all classes. Figure 4
depicts whole-slide visualizations of twelve biopsy samples using dots to
indicate predicted patch labels. Finally, CAM visualizations of individual
patches are shown in Figure 5 to highlight relevant features used in our
model’s classification process. A subjective qualitative investigation of
these visualizations by a gastrointestinal pathologist confirmed that the
predictions of our model are generally on target.

| Accuracy (%) | Precision (%) | Recall (%) | F1 Score (%)
---|---|---|---|---
Normal (n = 71) | 91.0 (87.2-94.9) | 83.3 (75.1-91.6) | 91.5 (85.1-98.0) | 87.2 (79.9-95.2)
Celiac Disease (n = 74) | 95.3 (92.4-98.1) | 90.0 (83.4-96.6) | 97.3 (93.6-99.9) | 93.5 (87.8-99.3)
Nonspecific Duodenitis (n = 67) | 89.2 (85.0-93.3) | 90.7 (83.0-98.5) | 73.1 (62.5-83.7) | 81.0 (71.5-90.5)
Average | 87.7 (83.3-92.2) | 88.0 (80.5-95.4) | 87.3 (79.5-95.2) | 87.2 (79.4-95.1)
Table 1: Performance of our final model for celiac disease detection on 212 duodenal biopsy whole-slide images in our test set. 95% confidence intervals are shown in parentheses. Prediction | Reference
---|---
| Normal | Celiac Disease | Duodenitis
Normal | 65 | 2 | 11
Celiac Disease | 1 | 72 | 7
Duodenitis | 5 | 0 | 49
Table 2: Confusion matrix of our final model for celiac disease detection on
212 duodenal biopsy whole-slide images in our test set. Fig. 3: ROC curves and
their area under the curves (AUC’s) for our model’s classifications on the
independent test set of 212 whole-slide biopsy images. Fig. 4: Visualization
of patch predictions of our model at the whole-slide level. A-D was correctly
classified as normal, E-H was correctly classified as celiac disease, and I-L
was correctly classified as nonspecific duodenitis.

##  4 DISCUSSION

Duodenal biopsies are the gold standard for confirming the diagnosis of CD.
The prevalence of CD has increased dramatically in recent years, and active
case-finding calls for more serological tests and duodenal biopsies. Detection
of CD on these biopsies could potentially be enhanced and facilitated by
automated image processing. In this study, we presented a deep learning model
that classifies duodenal tissue and highlights the associated features and
regions of interest. Previous work has used deep learning to detect CD from
endoscopic images [31, 32]. While acknowledging the substantive work of these
investigators, endoscopies are for the most part not used for confirming CD
diagnoses. Our model not only detects CD on duodenal biopsies, but it also
visualizes regions of normal tissue, celiac disease, and nonspecific
duodenitis to aid review by pathologists. Of note, we are not aware of any
other existing system for CD detection on biopsy images.

Our model achieved high performance for detection of celiac disease. On the
independent test set of 212 images, our model detected CD with a considerable
F1 score of 93.5% and AUC of 0.993. Since our model made predictions at the
patch level and then aggregated them for whole-slide inference, it was
relatively unaffected by noise and achieved high accuracy. For normal and
nonspecific duodenitis, F1 scores were 87.2% and 81.0% respectively.
Identification of nonspecific duodenitis was more challenging because this
class’s tissue often also contains some normal tissue fragments, which
complicate the analysis. Sixteen slides were misclassified between normal and
nonspecific duodenitis, and pathologist evaluation of these errors revealed
errors related to tissue orientation, fixation artifact, and patchy histologic
changes. In addition, seven slides of nonspecific duodenitis were identified
as celiac disease due to focal increase of intraepithelial lymphocytes and
partial villous atrophy. Performance measures for the nonspecific duodenitis
class were the lowest across the board. There are several reasons for this.
One could be that nonspecific duodenitis had the lowest number of training
samples, comprising only 64 images in the training set compared to 620 and 106
for normal tissue and celiac disease respectively. Another could be that this
category comprises several disease entities including peptic chronic
duodenitis, active duodenitis, and other nonspecific reactive changes, making
it more challenging to detect since there was a wider range of histologic
attributes to learn. Finally, slides labeled as nonspecific duodenitis often
contained some portions of normal tissue, and since we extracted patch labels
based on whole-slide labels during the training process, it is likely that
some mislabeled data was used in model training and made it harder to detect
this class in our approach.

In terms of visualization, the class activation mapping results of our model’s
selected areas of attention indicate that our model has learned the correct
histologic features for each class. Classification of normal tissue tended to
be holistic, with attention to almost all tissue area including normal villous
architecture. For celiac disease, the model correctly identified villous
atrophy, intraepithelial lymphocytosis, and chronic inflammation in the lamina
propria. In the case of nonspecific duodenitis, the model identified villous
thickening, Brunner’s gland hyperplasia, foveolar metaplasia, and chronic
inflammation.

Fig. 5: Class activation mapping (CAM) heat maps highlighting most informative
regions of patches relevant to normal, celiac disease, and nonspecific
duodenitis classes. Red regions indicate areas of attention for our residual
neural network. Classification of normal tissue tends to be holistic, with
attention to almost all tissue area including normal villous architecture. For
celiac disease, the model correctly identified villous atrophy,
intraepithelial lymphocytosis, and chronic inflammation in the lamina propria.
In the case of nonspecific duodenitis, the model identified villous
thickening, Brunner’s gland hyperplasia, foveolar metaplasia, and chronic
inflammation.

Our results indicate that deep neural networks have substantial potential to
aid gastrointestinal pathologists in diagnosing CD. For application in a
clinical setting, our model could be integrated into existing laboratory
information management systems to pre-populate patch predictions on slides and
provide preliminary diagnoses prior to review by pathologists. In addition, a
visualization of the slide evaluated by our model at the piecewise level could
highlight precise tissue area containing abnormal or sprue patterns, allowing
pathologists to quickly examine regions of interest. As CD prevalence has
increased dramatically in recent years, more serological screenings and
duodenal biopsies are being done for patients at risk [21, 22]. With biopsies
as the gold standard for diagnosis, our work aims to provide pathologists with
a tool for more accurate and efficient detection of CD.

The model presented in this paper is rooted in solid deep learning methodology
and achieves commendable performance, but there are several limitations of our
study. One limitation is that all biopsy slides were collected from a single
medical center and scanned with the same equipment, so our data may not be
representative of the entire range of histologic patterns in patients
worldwide. Although our whole-slide scans are high resolution and we were able
to extract a large number of patches for training with the sliding window
method, our dataset is still small in comparison to conventional datasets in
deep learning, which contain more than ten thousand unique samples per class
[43, 44] and more than a million unique images in total [45, 46]. Overfitting
is unlikely because we generated a large number of small patches for training
and conducted final evaluation on an independent test set, but it is still a
possibility. Collecting more data in collaboration with another medical center
in future work would allow us to train a more generalizable neural network and
could also improve our model’s performance in classifying nonspecific
duodenitis.

Moving forward, more work will be done to further the capabilities of our
model and evaluate its use a clinical setting. Collecting an annotated dataset
with specific histopathological classifications of celiac disease and labeled
bounding boxes around lesions would allow our model to classify and locate
specific lesion types, providing pathologists with more comprehensive slide
analysis, particularly for the nonspecific duodenitis class. Furthermore, once
more data is collected, we can predict slide level results by using patch
predictions to train a traditional machine learning classifier such as a
support vector machine or random forest, which may yield better results than
our current thresholding method. In terms of clinical application, we plan on
validating our model on a larger test set from multiple institutions and
deploying a trial implementation of our model into laboratory information
management systems at the Dartmouth-Hitchcock Medical Center to measure its
ability to improve CD detection accuracy and efficiency. However, widespread
clinical implementation of such artificial intelligence tools will require
major future steps, which our group will be undertaking. Any deep learning
models for computer-aided pathology must be thoroughly validated through
clinical trials and be proven to enhance outcomes. Such model must also not
impact the established workflow of pathologists or slow down the speed of
existing programs. Most importantly, deep learning models must be accurate and
gain the confidence of physicians, patients, and the medical community. In its
current state, artificial intelligence has the ability to analyze images and
make preliminary classifications, but much more work must be done before
patients and physicians will be able to trust computers to make medical
decisions. We believe the work presented in this paper is a preliminary step
in this direction.

In summary, we have demonstrated that deep learning can achieve high accuracy
in detecting CD in duodenal biopsies. Our model uses a state-of-the-art
residual neural network architecture for whole-slide classification and
achieved exemplary results on an independent test set of 212 whole-slide
images. As CD prevalence and screening increases, we expect our model could
assist pathologists in more accurate and efficient evaluation of duodenal
biopsy slides.

##  5 ACKNOWLEDGEMENTS

We thank Matthew Suriawinata for assistance with slide scanning and Sophie
Montgomery and Lamar Moss for their feedback on the manuscript. This research
was supported in part by a National Institutes of Health grant, P20GM104416.

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