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COMPUTATIONAL PATHOLOGY

Integrating context for superior cancer prognosis

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Weakly supervised deep-learning models for the analysis of whole-slide images from tumour biopsies perform better at prognostic tasks if the models incorporate context from the local microenvironment.

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Fig. 1: Incorporation of context in weakly supervised computational pathology.

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Authors and Affiliations

  1. Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Guillaume Jaume, Andrew H. Song & Faisal Mahmood

  2. Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    Guillaume Jaume, Andrew H. Song & Faisal Mahmood

  3. Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA

    Guillaume Jaume, Andrew H. Song & Faisal Mahmood

  4. Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA

    Guillaume Jaume, Andrew H. Song & Faisal Mahmood

  5. Harvard Data Science Initiative, Harvard University, Cambridge, MA, USA

    Faisal Mahmood

Authors
  1. Guillaume Jaume
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  2. Andrew H. Song
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  3. Faisal Mahmood
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Correspondence to Faisal Mahmood.

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Jaume, G., Song, A.H. & Mahmood, F. Integrating context for superior cancer prognosis. Nat. Biomed. Eng 6, 1323–1325 (2022). https://doi.org/10.1038/s41551-022-00924-z

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