Deep Learning for Visual Recognition of Environmental Enteropathy and Celiac Disease

Physicians use biopsies to distinguish between different but histologically similar enteropathies. The range of syndromes and pathologies that could cause different gastrointestinal conditions makes this a difficult problem. Recently, deep learning has been used successfully in helping diagnose canc...

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Bibliographic Details
Published in:arXiv.org 2019-08
Main Authors: Shrivastava, Aman, Kant, Karan, Sengupta, Saurav, Sung-Jun, Kang, Khan, Marium, Ali, Asad, Moore, Sean R, Amadi, Beatrice C, Kelly, Paul, Brown, Donald E, Syed, Sana
Format: Article
Language:English
Subjects:
Autoimmune diseases
Biopsy
Celiac disease
Deep learning
Digitization
Image filters
Machine learning
Neural networks
Physicians
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Summary:Physicians use biopsies to distinguish between different but histologically similar enteropathies. The range of syndromes and pathologies that could cause different gastrointestinal conditions makes this a difficult problem. Recently, deep learning has been used successfully in helping diagnose cancerous tissues in histopathological images. These successes motivated the research presented in this paper, which describes a deep learning approach that distinguishes between Celiac Disease (CD) and Environmental Enteropathy (EE) and normal tissue from digitized duodenal biopsies. Experimental results show accuracies of over 90% for this approach. We also look into interpreting the neural network model using Gradient-weighted Class Activation Mappings and filter activations on input images to understand the visual explanations for the decisions made by the model.
ISSN:2331-8422