Automatic detection of bowel disease with residual networks

Holland R, Patel U, Lung P, Chotzoglou E, Kainz B (2019)

Publication Type: Conference contribution

Publication year: 2019

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer

Book Volume: 11843 LNCS

Pages Range: 151-159

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Shenzhen, CHN

ISBN: 9783030322809

DOI: 10.1007/978-3-030-32281-6_16

Abstract

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.

Authors with CRIS profile

Bernhard Kainz

Involved external institutions

Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB) London North West Healthcare NHS Trust GB United Kingdom (GB)

How to cite

APA:

Holland, R., Patel, U., Lung, P., Chotzoglou, E., & Kainz, B. (2019). Automatic detection of bowel disease with residual networks. In Islem Rekik, Ehsan Adeli, Sang Hyun Park (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 151-159). Shenzhen, CHN: Springer.

MLA:

Holland, Robert, et al. "Automatic detection of bowel disease with residual networks." Proceedings of the 2nd International Workshop on Predictive Intelligence in Medicine, PRIME 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, Shenzhen, CHN Ed. Islem Rekik, Ehsan Adeli, Sang Hyun Park, Springer, 2019. 151-159.

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