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Medical AI

Improving image labelling quality

Nature Machine Intelligence volume 5, pages 335–336 (2023)Cite this article

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There is a continuing demand for high-quality, large-scale annotated datasets in medical imaging supported by machine learning. A new study investigates the importance of what type of instructions crowdsourced annotators receive.

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Fig. 1: Image annotation is often a laborious manual process.

Thomas Angus, Imperial College London

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Authors and Affiliations

  1. Department of Congenital Cardiology, Evelina London Children’s Healthcare, Guy’s and St Thomas’ NHS Foundation Trust, London, UK

    Thomas G. Day, John M. Simpson & Reza Razavi

  2. Faculty of Life Sciences and Medicine, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK

    Thomas G. Day, John M. Simpson & Reza Razavi

  3. Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany

    Bernhard Kainz

  4. Department of Computing, Faculty of Engineering, Imperial College London, London, UK

    Bernhard Kainz

Authors
  1. Thomas G. Day
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  2. John M. Simpson
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  3. Reza Razavi
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  4. Bernhard Kainz
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Corresponding authors

Correspondence to Thomas G. Day or Bernhard Kainz.

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The authors declare no competing interests.

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Day, T.G., Simpson, J.M., Razavi, R. et al. Improving image labelling quality. Nat Mach Intell 5, 335–336 (2023). https://doi.org/10.1038/s42256-023-00645-1

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