Integrating context for superior cancer prognosis (original) (raw)
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- Published: 18 August 2022
COMPUTATIONAL PATHOLOGY
Nature Biomedical Engineering volume 6, pages 1323–1325 (2022)Cite this article
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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.
References
- Bera, K. et al. Nat. Rev. Clin. Oncol. 16, 703–715 (2019).
Article Google Scholar - Coudray, N. et al. Nat. Med. 24, 1559–1567 (2018).
Article CAS Google Scholar - Campanella, G. et al. Nat. Med. 25, 1301–1309 (2019).
Article CAS Google Scholar - Lu, M. Y. et al. Nat. Biomed. Eng. 5, 555–570 (2021).
Article Google Scholar - Chen, R. J. et al. Whole slide images are 2D point clouds: context-aware survival prediction using patch-based graph convolutional networks. In MICCAI 2021: Medical Image Computing and Computer Assisted Intervention (eds de Bruijne, M. et al.) 339–349 (Springer, 2021).
- Lee, Y. et al. Nat. Biomed. Eng. https://doi.org/10.1038/s41551-022-00923-0 (2022).
Article Google Scholar - Jaume, G. et al. Quantifying explainers of graph neural networks in computational pathology. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (eds IEEE staff) 8102–8112 (IEEE, 2021).
- Dosovitskiy, A. et al. An image is worth 16x16 words: transformers for image recognition at scale. In International Conference on Learning Representations 2021 Paper 1909 (ICLR, 2021).
- Shao, Z. C. et al. TransMIL: transformer based correlated multiple instance learning for whole slide image classification. In Advances in Neural Information Processing Systems 34 (eds Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P. S. & Wortman Vaughan, J.) 2136–2147 (NeurIPS, 2021).
- Chen, R. J. et al. Scaling vision transformers to gigapixel images via hierarchical self-supervised learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (eds IEEE staff) 16144–16155 (IEEE, 2022).
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Authors and Affiliations
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
Guillaume Jaume, Andrew H. Song & Faisal Mahmood - Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
Guillaume Jaume, Andrew H. Song & Faisal Mahmood - Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA
Guillaume Jaume, Andrew H. Song & Faisal Mahmood - Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
Guillaume Jaume, Andrew H. Song & Faisal Mahmood - Harvard Data Science Initiative, Harvard University, Cambridge, MA, USA
Faisal Mahmood
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- Guillaume Jaume
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Correspondence toFaisal 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
- Published: 18 August 2022
- Issue Date: December 2022
- DOI: https://doi.org/10.1038/s41551-022-00924-z
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