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dc.contributor.authorXingzhi, Yue
dc.contributor.authorDimitriou, Neofytos
dc.contributor.authorCaie, Peter David
dc.contributor.authorHarrison, David James
dc.contributor.authorArandelovic, Ognjen
dc.identifier.citationXingzhi , 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 . < >en
dc.identifier.otherPURE: 258788683
dc.identifier.otherPURE UUID: 60651115-f69c-47ac-bff7-ffd319374479
dc.description.abstractDigital 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.
dc.relation.ispartofArXiv e-printsen
dc.rightsCopyright 2019 the Author(s).en
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectRB Pathologyen
dc.subjectRC0254 Neoplasms. Tumors. Oncology (including Cancer)en
dc.titleColorectal cancer outcome prediction from H&E whole slide images using machine learning and automatically inferred phenotype profilesen
dc.typeJournal articleen
dc.contributor.institutionUniversity of St Andrews.School of Medicineen
dc.contributor.institutionUniversity of St Andrews.Sir James Mackenzie Institute for Early Diagnosisen
dc.contributor.institutionUniversity of St Andrews.Cellular Medicine Divisionen
dc.contributor.institutionUniversity of St Andrews.School of Computer Scienceen
dc.description.statusNon peer revieweden

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