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COMPUTATIONAL HISTOPATHOLOGY

No pixel-level annotations needed

Nature Biomedical Engineering volume 3, pages 855–856 (2019)Cite this article

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A deep-learning model for cancer detection trained on a large number of scanned pathology slides and associated diagnosis labels enables model development without the need for pixel-level annotations.

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Fig. 1: Manual pixel-level annotations are challenging to perform at scale.
Fig. 2: Tumour diagnosis via MIL in the absence of pixel-level annotations.

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Authors and Affiliations

  1. Department of Pathology, Radboud University Medical Center Nijmegen, Nijmegen, the Netherlands

    Jeroen van der Laak, Francesco Ciompi & Geert Litjens

  2. Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden

    Jeroen van der Laak

Authors
  1. Jeroen van der Laak
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  2. Francesco Ciompi
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  3. Geert Litjens
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Corresponding author

Correspondence to Jeroen van der Laak.

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Competing interests

Jeroen van der Laak is a member of the scientific advisory boards of Philips (The Netherlands) and ContextVision (Sweden), and receives research funding from Philips (The Netherlands) and from Sectra (Sweden). The remaining authors declare no competing interests.

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van der Laak, J., Ciompi, F. & Litjens, G. No pixel-level annotations needed. Nat Biomed Eng 3, 855–856 (2019). https://doi.org/10.1038/s41551-019-0472-6

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