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

      , , , , , ,

      Clinical Radiology

      Elsevier BV

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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.

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          Author and article information

          Journal
          Clinical Radiology
          Clinical Radiology
          Elsevier BV
          00099260
          April 2019
          April 2019
          Article
          10.1016/j.crad.2019.04.002
          6815686
          31040006
          © 2019

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