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Currently, colon cancer diagnosis is based on manual assessment of tissue samples stained with hematoxylin and eosin (H&E). This is a high volume, time consuming, and subjective task which could be aided by automatic cancer detection. We propose an algorithm for automatic cancer detection within WSI H&E stains using a multi class colon tissue classifier based on features extracted from 5 different color representations. Approx. 32000 tissue patches were extracted for the classifier from manual annotations of 9 representative colon tissue types from 74 WSI H&E stains. Colon tissue classifiers based on gray level or color features were trained using leave-one-out forward selection. The best colon tissue classifier was based on color texture features obtaining an average tissue precision-recall (PR) area under the curve (AUC) of 0.886 and a cancer PR-AUC of 0.950 on 20 validation WSI H&E stains.
|Title of host publication||Computational Pathology and Ophthalmic Medical Image Analysis : First International Workshop, COMPAY 2018, and 5th International Workshop, OMIA 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, 16-20 September 2018, Proceedings|
|Editors||Zeike Taylor, Hrvoje Bogunovic, David Snead, Mona K. Garvin, Xin Jan Chen, Francesco Ciompi, Yanwu Xu, Lena Maier-Hein, Mitko Veta, Emanuele Trucco, Danail Stoyanov, Nasir Rajpoot, Jeroen van der Laak, Anne Martel, Stephen McKenna|
|Number of pages||8|
|Publication status||Published - 2018|
|Event||1st International Workshop on Computational Pathology, COMPAY 2018, and 5th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2018 - Granada, Spain|
Duration: 16 Oct 2018 → 20 Oct 2018
|Conference||1st International Workshop on Computational Pathology, COMPAY 2018, and 5th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2018|
|Period||16/10/2018 → 20/10/2018|
|Series||Lecture Notes in Computer Science|
- H&E stain
- Machine learning
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01/12/2015 → …