Context integration for better cancer prognosis

  • Bera, K. et al. Nat. Rev. Clin. Oncol. 16703–715 (2019).

    Google Scholar article

  • Coudray, N. et al. Nat. Med. 241559-1567 (2018).

    CAS Google Scholar Article

  • Campanella, G. et al. Nat. Med. 251301-1309 (2019).

    CAS Google Scholar Article

  • Lu, MY et al. Nat. Biomedical. Eng. 5555–570 (2021).

    Google Scholar article

  • Chen, RJ et al. Whole slide images are 2D scatter plots: contextual survival prediction using patch-based graph convolutional networks. In MICCAI 2021: Medical Imaging and Computer-Assisted Intervention (eds de Bruijne, M. et al.) 339–349 (Springer, 2021).

  • Lee, Y. et al. Nat. Biomedical. Eng. (2022).

    PubMed Google Scholar article

  • Jaume, G. et al. Quantifying neural network explainers of graphs in computational pathology. In IEEE/CVF Conference on Computer Vision and Pattern recognition (IEEE eds staff) 8102–8112 (IEEE, 2021).

  • Dosovitskiy, A. et al. A picture is worth 16×16 words: transformers for large-scale image recognition. In International Conference on Learning Representations 2021 Document 1909 (ICLR, 2021).

  • Shao, ZC et al. TransMIL: Transformer-based correlated multiple instance learning for entire slide image classification. In Advances in Neural Information Processing Systems 34 (eds Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, PS & Wortman Vaughan, J.) 2136–2147 (NeurIPS, 2021).

  • Chen, RJ et al. Scaling vision transformers into 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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