Fully-Automated CNN-Based Computer Aided Celiac Disease Diagnosis

While a significant amount of research has been on computer aided diagnosis of celiac disease, challenges remain especially due to difficult imaging conditions during endoscopy which frequently result in image degradations. To compensate for these degradations which often hide relevant disease markers, classification trials so far have been performed exclusively utilizing informative patches, which were manually selected by experienced physicians. In this work, we propose a novel fully-automated method to obtain decisions from computer aided diagnosis systems without any interaction, based on original endoscopic image data. For this purpose, we rely on a discriminative model based on convolutional neural networks trained with informative patch data. Additionally, we fit a probabilistic model utilizing original endoscopic image data to obtain realistic predictions for patches concerning their level of reliability. In our experiments, the state-of-the-art considering a classification on image as well as on patient level is outperformed.