  Crohn's disease, one of two inflammatory bowel diseases (IBD), affects
200,000 people in the UK alone, or roughly one in every 500. We explore the
feasibility of deep learning algorithms for identification of terminal ileal
Crohn's disease in Magnetic Resonance Enterography images on a small dataset.
We show that they provide comparable performance to the current clinical
standard, the MaRIA score, while requiring only a fraction of the preparation
and inference time. Moreover, bowels are subject to high variation between
individuals due to the complex and free-moving anatomy. Thus we also explore
the effect of difficulty of the classification at hand on performance. Finally,
we employ soft attention mechanisms to amplify salient local features and add
interpretability.
