Journal of Pathology Informatics

ORIGINAL ARTICLE
Year
: 2020  |  Volume : 11  |  Issue : 1  |  Page : 28-

Colorectal cancer detection based on deep learning


Lin Xu1, Blair Walker2, Peir-In Liang3, Yi Tong1, Cheng Xu1, Yu Chun Su1, Aly Karsan4 
1 GenerationsE Software Solutions, Inc., Surrey, Canada
2 Department of Pathology, St. Paul's Hospital, University of British Columbia, Vancouver, Canada
3 Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
4 Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, Canada

Correspondence Address:
Dr. Aly Karsan
Room 9-111, 675 W 10th Ave, Vancouver, BC V5Z 1L3
Canada

Introduction: The initial point in the diagnostic workup of solid tumors remains manual, with the assessment of hematoxylin and eosin (H&E)-stained tissue sections by microscopy. This is a labor-intensive step that requires attention to detail. In addition, diagnoses are influenced by an individual pathologist's knowledge and experience and may not always be reproducible between pathologists. Methods: We introduce a deep learning-based method in colorectal cancer detection and segmentation from digitized H&E-stained histology slides. Results: In this study, we demonstrate that this neural network approach produces median accuracy of 99.9% for normal slides and 94.8% for cancer slides compared to pathologist-based diagnosis on H&E-stained slides digitized from clinical samples. Conclusion: Given that our approach has very high accuracy on normal slides, use of neural network algorithms may provide a screening approach to save pathologist time in identifying tumor regions. We suggest that this new method may be a powerful assistant for colorectal cancer diagnostics.


How to cite this article:
Xu L, Walker B, Liang PI, Tong Y, Xu C, Su YC, Karsan A. Colorectal cancer detection based on deep learning.J Pathol Inform 2020;11:28-28


How to cite this URL:
Xu L, Walker B, Liang PI, Tong Y, Xu C, Su YC, Karsan A. Colorectal cancer detection based on deep learning. J Pathol Inform [serial online] 2020 [cited 2020 Sep 30 ];11:28-28
Available from: http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2020;volume=11;issue=1;spage=28;epage=28;aulast=Xu;type=0