Open access image repositories: high-quality data to enable machine learning research
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UMass Chan Affiliations
Department of Radiation OncologyDocument Type
Journal ArticlePublication Date
2020-01-01Keywords
quantitative image analysismachine learning
image repositories
Cancer Imaging Archive
Artificial Intelligence and Robotics
Bioinformatics
Databases and Information Systems
Health Information Technology
Neoplasms
Oncology
Radiation Medicine
Radiology
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Show full item recordAbstract
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.Source
Prior F, Almeida J, Kathiravelu P, Kurc T, Smith K, Fitzgerald TJ, Saltz J. Open access image repositories: high-quality data to enable machine learning research. Clin Radiol. 2020 Jan;75(1):7-12. doi: 10.1016/j.crad.2019.04.002. Epub 2019 Apr 28. PMID: 31040006; PMCID: PMC6815686. Link to article on publisher's site
DOI
10.1016/j.crad.2019.04.002Permanent Link to this Item
http://hdl.handle.net/20.500.14038/47903PubMed ID
31040006Related Resources
ae974a485f413a2113503eed53cd6c53
10.1016/j.crad.2019.04.002