Improving image labelling quality

Day TG, Simpson JM, Razavi R, Kainz B (2023)

Publication Type: Journal article, Editorial

Publication year: 2023

Journal

Nature Machine Intelligence Nature Research

Book Volume: 5

Pages Range: 335-336

Journal Issue: 4

DOI: 10.1038/s42256-023-00645-1

Abstract

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.

Authors with CRIS profile

Bernhard Kainz Professur für Image Data Exploration and Analysis

Involved external institutions

Guy's and St Thomas' (NHS Foundation Trust) GB United Kingdom (GB)

How to cite

APA:

Day, T.G., Simpson, J.M., Razavi, R., & Kainz, B. (2023). Improving image labelling quality. Nature Machine Intelligence, 5(4), 335-336. https://dx.doi.org/10.1038/s42256-023-00645-1

MLA:

Day, Thomas G., et al. "Improving image labelling quality." Nature Machine Intelligence 5.4 (2023): 335-336.

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