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Abstract
Originally motivated by the need for research reproducibility and data reuse, large-scale,
open access information repositories have become key resources for training and testing
of advanced machine learning applications in biomedical and clinical research. To
be of value, such repositories must provide large, high-quality data sets, where quality
is defined as minimising variance due to data collection protocols and data misrepresentations.
Curation is the key to quality. We have constructed a large public access image repository,
The Cancer Imaging Archive, dedicated to the promotion of open science to advance
the global effort to diagnose and treat cancer. Drawing on this experience and our
experience in applying machine learning techniques to the analysis of radiology and
pathology image data, we will review the requirements placed on such information repositories
by state-of-the-art machine learning applications and how these requirements can be
met.