Joint Multi Organ and Tumor Segmentation from Partial Labels Using Federated Learning-Reference-Cited by-同舟云学术

Joint Multi Organ and Tumor Segmentation from Partial Labels Using Federated Learning

Published:2022 Issue: Volume: Page:58-67
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
Short-container-title:

Author:

Shen Chen,Wang Pochuan,Yang Dong,Xu Daguang,Oda Masahiro,Chen Po-Ting,Liu Kao-Lang,Liao Wei-Chih,Fuh Chiou-Shann,Mori Kensaku,Wang Weichung,Roth Holger R.

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-18523-6_6

Reference21 articles.

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2. Chen, S., Ma, K., Zheng, Y.: Med3D: transfer learning for 3d medical image analysis. arXiv preprint arXiv:1904.00625 (2019)

3. Dayan, I., et al.: Federated learning for predicting clinical outcomes in patients with Covid-19. Nat. Med. 27(10), 1735–1743 (2021)

4. Heller, N., et al.: The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KITS19 challenge. Med. Image Anal. 67, 101821 (2021). https://doi.org/10.1016/j.media.2020.101821

5. Heller, N., et al.: The KITS19 challenge data: 300 kidney tumor cases with clinical context, CT semantic segmentations, and surgical outcomes (2019). https://doi.org/10.48550/ARXIV.1904.00445, https://arxiv.org/abs/1904.00445

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