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Research at St Andrews

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

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


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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 digital 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, and numerous others. In this paper we introduce a novel machine learning based framework for the prediction of colorectal cancer outcome from whole 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
Subtitle of host publicationHonolulu; United States; 18 March 2019 through 20 March 2019
EditorsOliver Eulenstein, Hisham Al-Mubaid, Qin Ding
Number of pages11
Publication statusPublished - 18 Mar 2019
Event11th International Conference on Bioinformatics and Computational Biology (BICOB) - Waikiki Beach Marriott Resort, Honolulu, United States
Duration: 18 Mar 201920 Mar 2019
Conference number: 11

Publication series

NameEPiC Series in Computing
ISSN (Electronic)2398-7340


Conference11th International Conference on Bioinformatics and Computational Biology (BICOB)
Abbreviated titleBICOB
Country/TerritoryUnited States
Internet address

    Research areas

  • CNN, Convolution, Health, Neural network, Pathology, Public, Tumour

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ID: 259313993