Enhancing Cross-Institute Generalisation of GNNs in Histopathology Through Multiple Embedding Graph Augmentation (MEGA)

Author:

Campbell JonathanORCID,Vanea ClaudiaORCID,Salumäe Liis,Meir Karen,Hochner-Celnikier Drorith,Hochner HagitORCID,Laisk TriinORCID,Ernst Linda M.ORCID,Lindgren Cecilia M.ORCID,Xie WeidiORCID,Nellåker ChristofferORCID

Publisher

Springer Nature Switzerland

Reference37 articles.

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2. Bándi, P., et al.: From detection of individual metastases to classification of lymph node status at the patient level: the CAMELYON17 challenge. IEEE Trans. Med. Imaging 38(2), 550–560 (2019). Conference Name: IEEE Transactions on Medical Imaging. https://doi.org/10.1109/TMI.2018.2867350

3. Cai, T., Luo, S., Xu, K., He, D., Liu, T.Y., Wang, L.: GraphNorm: a principled approach to accelerating graph neural network training. In: Meila, M., Zhang, T. (eds.) Proceedings of the 38th International Conference on Machine Learning. Proceedings of Machine Learning Research, July 2021, vol. 139, pp. 1204–1215. PMLR (2021)

4. Chiang, W.L., Liu, X., Si, S., Li, Y., Bengio, S., Hsieh, C.J.: Cluster-GCN: an efficient algorithm for training deep and large graph convolutional networks. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, pp. 257–266. Association for Computing Machinery, New York (2019). Event-place: Anchorage, AK, USA. https://doi.org/10.1145/3292500.3330925

5. Chlipala, E.A., et al.: Impact of preanalytical factors during histology processing on section suitability for digital image analysis. Toxicol. Pathol. 49(4), 755–772 (2021). Publisher: SAGE Publications Inc. https://doi.org/10.1177/0192623320970534

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