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

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