  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.
