Colorectal cancer outcome prediction from H&E whole slide images using machine learning and automatically inferred phenotype profiles

Xingzhi Yue, Neofytos Dimitriou, Peter D. Caie, David J. Harrison, Ognjen Arandjelović

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Digital pathology (DP) is a new research area which falls under the broad umbrella of health informatics. Owing to its potential for major public health impact, in recent years DP has been attracting much research attention. Nevertheless, a wide breadth of significant conceptual and technical challenges remain, few of them greater than those encountered in the field of oncology. The automatic analysis of digital pathology slides of cancerous tissues is particularly problematic due to the inherent heterogeneity of the disease, extremely large images, amongst numerous others. In this paper we introduce a novel machine learning based framework for the prediction of colorectal cancer outcome from whole digitized haematoxylin & eosin (H&E) stained histopathology slides. Using a real-world data set we demonstrate the effectiveness of the method and present a detailed analysis of its different elements which corroborate its ability to extract and learn salient, discriminative, and clinically meaningful content.

Original languageEnglish
Title of host publicationProceedings of 11th International Conference on Bioinformatics and Computational Biology, BiCOB 2019
EditorsOliver Eulenstein, Hisham Al-Mubaid, Qin Ding
PublisherInternational Society for Computers and Their Applications (ISCA)
Pages139-149
Number of pages11
ISBN (Electronic)9781510885950
Publication statusPublished - 18 Mar 2019
Event11th International Conference on Bioinformatics and Computational Biology, BiCOB 2019 - Honolulu, United States
Duration: 18 Mar 201920 Mar 2019

Publication series

NameProceedings of 11th International Conference on Bioinformatics and Computational Biology, BiCOB 2019

Conference

Conference11th International Conference on Bioinformatics and Computational Biology, BiCOB 2019
Country/TerritoryUnited States
CityHonolulu
Period18/03/1920/03/19

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