Colorectal Cancer Outcome Prediction from H&E Whole Slide Images using Machine Learning and Automatically Inferred Phenotype Profiles

11 pages•Published: March 18, 2019

Xingzhi Yue, Neofytos Dimitriou, Peter Caie, David Harrison and Ognjen Arandjelovic

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 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.

Keyphrases: cnn, convolution, health, neural network, pathology, public, tumour

In: Oliver Eulenstein, Hisham Al-Mubaid and Qin Ding (editors). Proceedings of 11th International Conference on Bioinformatics and Computational Biology, vol 60, pages 139-149.

Links:	https://easychair.org/publications/paper/M7NT
	https://doi.org/10.29007/n912

BibTeX entry

@inproceedings{BiCOB2019:Colorectal_Cancer_Outcome_Prediction,
  author    = {Xingzhi Yue and Neofytos Dimitriou and Peter Caie and David Harrison and Ognjen Arandjelovic},
  title     = {Colorectal Cancer Outcome Prediction from H&E Whole Slide Images using Machine Learning and Automatically Inferred Phenotype Profiles},
  booktitle = {Proceedings of 11th International Conference on Bioinformatics and Computational Biology},
  editor    = {Oliver Eulenstein and Hisham Al-Mubaid and Qin Ding},
  series    = {EPiC Series in Computing},
  volume    = {60},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/M7NT},
  doi       = {10.29007/n912},
  pages     = {139-149},
  year      = {2019}}

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