Open access image repositories: high-quality data to enable machine learning research

• Authors: F. Prior, University of Arkansas Medical Sciences; J. Almeida, National Cancer Institute; P. Kathiravelu, Emory University; Tahsin Kurc, Emory University; K. Smith, University of Arkansas Medical Sciences; T.J. Fitzgerald, University of Massachusetts; J. Saltz, SUNY Stony Brook
• Journal Title: CLINICAL RADIOLOGY
• Volume: Volume 75, Number 1
• Publisher: W B SAUNDERS CO LTD | 2020-01-01, Pages 7-12
• Type of Work: Article | Post-print: After Peer Review
• Publisher DOI: http://doi.org/10.1016/j.crad.2019.04.002
• Subjects: Health Sciences, Oncology; Health Sciences, Radiology
• Keywords: Science & Technology; Life Sciences & Biomedicine; Radiology, Nuclear Medicine & Medical Imaging; COMPUTER-AIDED DIAGNOSIS; MEDICAL IMAGES; LUNG NODULES; RADIOMICS; MANAGEMENT; CAD; IDENTIFICATION; INFORMATION; VALIDATION; ALGORITHM

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.