Diagnosis and Analysis of Celiac Disease and Environmental Enteropathy on Biopsy Images using Deep Learning Approaches

Abstract

Celiac Disease (CD) and Environmental Enteropathy (EE) are common causes of malnutrition and adversely impact normal childhood development. Both conditions require a tissue biopsy for diagnosis and a major challenge of interpreting clinical biopsy images to differentiate between these gastrointestinal diseases is striking histopathologic overlap between them. In the current study, we propose four diagnosis techniques for these diseases and address their limitations and advantages. First, the diagnosis between CD, EE, and Normal biopsies is considered, but the main challenge with this diagnosis technique is the staining problem. The dataset used in this research is collected from different centers with different staining standards. To solve this problem, we use color balancing in order to train our model with a varying range of colors. Random Multimodel Deep Learning (RMDL) architecture has been used as another approach to mitigate the effects of the staining problem. RMDL combines different architectures and structures of deep learning and the final output of the model is based on the majority vote. CD is a chronic autoimmune disease that affects the small intestine genetically predisposed children and adults. Typically, CD rapidly progress from Marsh I to IIIa. Marsh III is sub-divided into IIIa (partial villus atrophy), Marsh IIIb (subtotal villous atrophy), and Marsh IIIc (total villus atrophy) to explain the spectrum of villus atrophy along with crypt hypertrophy and increased intraepithelial lymphocytes. In the second part of this study, we proposed two ways for diagnosing different stages of CD. Finally, in the third part of this study, these two steps are combined as Hierarchical Medical Image Classification (HMIC) to have a model to diagnose the disease data hierarchically.

arXiv commentPhD dissertation, Univ Virginia (May 2020)

References in corpus (22)

• Adam: A Method for Stochastic Optimization
• A Survey on Deep Learning in Medical Image Analysis
• Sequence to Sequence Learning with Neural Networks
• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
• Machine Learning in Automated Text Categorization
• On the difficulty of training Recurrent Neural Networks
• Learning Important Features Through Propagating Activation Differences
• Opening the Black Box of Deep Neural Networks via Information
• HDLTex: Hierarchical Deep Learning for Text Classification
• An Autoencoder Approach to Learning Bilingual Word Representations
• Effective Use of Word Order for Text Categorization with Convolutional Neural Networks
• Patient2Vec: A Personalized Interpretable Deep Representation of the Longitudinal Electronic Health Record
• Deep EndoVO: A Recurrent Convolutional Neural Network (RCNN) based Visual Odometry Approach for Endoscopic Capsule Robots
• Medical Image Classification via SVM using LBP Features from Saliency-Based Folded Data
• Analysis of Railway Accidents' Narratives Using Deep Learning
• An Improvement of Data Classification Using Random Multimodel Deep Learning (RMDL)
• Doubly Convolutional Neural Networks
• One-shot and few-shot learning of word embeddings
• Weighted Unsupervised Learning for 3D Object Detection
• Neural Stain Normalization and Unsupervised Classification of Cell Nuclei in Histopathological Breast Cancer Images
• Automated detection of celiac disease on duodenal biopsy slides: a deep learning approach