Deep learning for visual recognition of environmental enteropathy and celiac disease
Paediatrics and Child Health; Women and Child Health
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.
IEEE EMBS International Conference on Biomedical & Health Informatics (BHI)
Moore, S. R.,
Amadi, B. C.,
Brown, D. E.,
(2019). Deep learning for visual recognition of environmental enteropathy and celiac disease. IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), 1-4.
Available at: https://ecommons.aku.edu/pakistan_fhs_mc_women_childhealth_paediatr/866