Imperial College London
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DR BERNHARD KAINZ

Faculty of Engineering, Department of Computing

Reader in Medical Image Computing
 
 
 
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Contact

 

+44 (0)20 7594 8349b.kainz Website CV

 
 
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Location

 

372Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Holland:2020:10.1007/978-3-030-32281-6_16,
author = {Holland, R and Patel, U and Lung, P and Chotzoglou, E and Kainz, B},
doi = {10.1007/978-3-030-32281-6_16},
pages = {151--159},
publisher = {Springer International Publishing},
title = {Automatic detection of bowel disease with residual networks},
url = {http://dx.doi.org/10.1007/978-3-030-32281-6_16},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - 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.
AU  - Holland,R
AU  - Patel,U
AU  - Lung,P
AU  - Chotzoglou,E
AU  - Kainz,B
DO  - 10.1007/978-3-030-32281-6_16
EP  - 159
PB  - Springer International Publishing
PY  - 2020///
SN  - 0302-9743
SP  - 151
TI  - Automatic detection of bowel disease with residual networks
UR  - http://dx.doi.org/10.1007/978-3-030-32281-6_16
UR  - http://hdl.handle.net/10044/1/83338
ER  -
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