Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

Inflammatory bowel disease (IBD), in particular ulcerative colitis (UC), is required to be graded by endoscopists and is important for therapy monitoring. However, the accuracy of current endoscopic characterisation is operator dependant and can cause heterogeneous scoring leading to undesirable clinical outcomes for patients with IBD. Deep learning classification model for UC grading can be extremely helpful to clinicians allowing them for a comprehensive disease risk stratification. A systematic and continuous scoring is needed for better IBD patient stratification. While most methods in literature present a binary UC scoring, we propose a 3-way Mayo endoscopic subscore (MES) classification. In this context, we use an additive angular margin loss function in addition to cross-entropy loss to improve our model accuracy. We have evaluated multiple classification models and we demonstrate that the use of model scaling network together with added angular margin loss can provide improved results (over 10% improvement compared to ResNet-152).

Original publication

DOI

10.1109/ISBI52829.2022.9761437

Type

Conference paper

Publication Date

01/01/2022

Volume

2022-March