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.
@article{arxiv.1908.03272,
title = {Deep Learning for Visual Recognition of Environmental Enteropathy and Celiac Disease},
author = {Aman Shrivastava and Karan Kant and Saurav Sengupta and Sung-Jun Kang and Marium Khan and Asad Ali and Sean R. Moore and Beatrice C. Amadi and Paul Kelly and Donald E. Brown and Sana Syed},
journal= {arXiv preprint arXiv:1908.03272},
year = {2019}
}