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

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1902.03582.pdf (3.851Mb)
Date
09/03/2019
Author
Xingzhi, Yue
Dimitriou, Neofytos
Caie, Peter David
Harrison, David James
Arandelovic, Ognjen
Keywords
QA75 Electronic computers. Computer science
RB Pathology
RC0254 Neoplasms. Tumors. Oncology (including Cancer)
3rd-NDAS
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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.
Citation
Xingzhi , Y , Dimitriou , N , Caie , P D , Harrison , D J & Arandelovic , O 2019 , ' Colorectal cancer outcome prediction from H &E whole slide images using machine learning and automatically inferred phenotype profiles ' , ArXiv e-prints . < https://arxiv.org/abs/1902.03582 >
Publication
ArXiv e-prints
Status
Non peer reviewed
Type
Journal article
Rights
Copyright 2019 the Author(s).
Collections
  • University of St Andrews Research
URL
https://arxiv.org/abs/1902.03582
URI
http://hdl.handle.net/10023/17622

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