Automatic detection of bowel disease with residual networks
File(s)
Accepted version
OA Location
Author(s)
Holland, Robert
Patel, Uday
Lung, Phillip
Chotzoglou, Elisa
Kainz, Bernhard
Type
Conference Paper
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.
Date Issued
2020-10-10
Date Acceptance
2019-08-20
Citation
PRIME 2019: Predictive Intelligence in Medicine, 2020, 11843 LNCS, pp.151-159
ISBN
9783030322809
ISSN
0302-9743
Publisher
Springer International Publishing
Start Page
151
End Page
159
Journal / Book Title
PRIME 2019: Predictive Intelligence in Medicine
Volume
11843 LNCS
Copyright Statement
© Springer Nature Switzerland AG 2019. The final publication is available at Springer via https://link.springer.com/chapter/10.1007%2F978-3-030-32281-6_16
Source
International Workshop on PRedictive Intelligence In MEdicine
Subjects
Artificial Intelligence & Image Processing
Publication Status
Published
Start Date
2019-10-13
Coverage Spatial
Shenzhen, China
Date Publish Online
2019-10-10