Multi-task Federated Learning for Heterogeneous Pancreas Segmentation
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Federated learning provides an alternative paradigm for accurate and generalizable machine learning by only sharing numerical model updates from decentralized data.
“Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates.”
From Federated learning enables big data for rare cancer boundary detection (2022) · cites this paper as [20]
Cites
Attention U-Net: Learning Where to Look for the Pancreas
Oktay, O., Schlemper, J., Folgoc, L. L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N. Y., Kainz, B., et al. (2018) · arXiv · doi:10.48550/arxiv.1804.03999 · Cited by 5,458 · see citing works
Self-citationFederated Optimization in Heterogeneous Networks
Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., & Smith, V. (2018) · arXiv · doi:10.48550/arxiv.1812.06127 · Cited by 4,023 · see citing works
End-To-End Multi-Task Learning With Attention
Liu, S., Johns, E., & Davison, A. J. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00197 · Cited by 1,299 · see citing works
DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation
Roth, H. R., Lu, L., Farag, A., Shin, H.-C., Liu, J., Turkbey, E. B., & Summers, R. M. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-24553-9_68 · Cited by 784 · see citing works
Self-citationSimpson, A. L., Antonelli, M., Bakas, S., Bilello, M., Farahani, K., van Ginneken, B., Kopp‐Schneider, A., Landman, B. A., Litjens, G. J. S., Menze, B. H., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.09063 · Cited by 703 · see citing works
Sheller, M. J., Reina, G. A., Edwards, B., Martin, J., & Bakas, S. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-11723-8_9 · Cited by 609 · see citing works
Privacy-Preserving Federated Brain Tumour Segmentation
Li, W., Milletarì, F., Xu, D., Rieke, N., Hancox, J., Zhu, W., Baust, M., Cheng, Y., Ourselin, S., Cardoso, M. J., et al. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-32692-0_16 · Cited by 578 · see citing works
Self-citationDynamic Task Prioritization for Multitask Learning
Guo, M., Haque, A., Huang, D.-A., Yeung, S. Y., & Fei-Fei, L. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01270-0_17 · Cited by 377 · see citing works
A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans
Zhou, Y., Xie, L., Shen, W., Wang, Y., Fishman, E. K., & Yuille, A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66182-7_79 · Cited by 310 · see citing works
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Self-citationDou, Q., So, T. Y., Jiang, M., Liu, Q., Vardhanabhuti, V., Kaissis, G., Li, Z., Si, W., Lee, H. H. C., Yu, K., et al. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00431-6 · Cited by 278 · see citing works
Deep Q Learning Driven CT Pancreas Segmentation With Geometry-Aware U-Net
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C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation
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Self-citationFederated Learning used for predicting outcomes in SARS-COV-2 patientsPreprint
Flores, M. G., Dayan, I., Roth, H. R., et al. (2021) · Research Square · doi:10.21203/rs.3.rs-126892/v1 · Cited by 47 · see citing works
Self-citationAuto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image SegmentationPreprint
Xia, Y., Yang, D., Li, W., Myronenko, A., Xu, D., Obinata, H., Mori, H., An, P., Harmon, S. A., Turkbey, E. B., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.10195 · Cited by 39 · see citing works
Self-citationAutomated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning
Wang, P., Shen, C., Roth, H. R., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60548-3_19 · Cited by 38 · see citing works
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C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image SegmentationPreprint
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Federated learning enables big data for rare cancer boundary detection
Pati, S., Baid, U., Edwards, B., Sheller, M., Wang, S.-H., Reina, G. A., Foley, P., Gruzdev, A., Karkada, D., Davatzikos, C., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-33407-5 · Cited by 394 · see citing works
Rauniyar, A., Hagos, D. H., Jha, D., Håkegård, J. E., Bagci, U., Rawat, D. B., & Vlassov, V. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3329061 · Cited by 315 · see citing works
- Authors
- Shen, C., Wang, P., Roth, H. R., Yang, D., Xu, D., Oda, M., Wang, W., Fuh, C.-S., Chen, P.-T., Liu, K.-L., Liao, W.-C., & Mori, K.
- Affiliations
- Nagoya University (JP)
- National Taiwan University (TW)
- Nvidia (United States) (US)
- National Taiwan University Hospital (TW)
- ORCID
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2021
- Pages
- 101-110
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3195545894
- Cited by
- 36
- References
- 18
- Citations by year
- 2026: 3 · 2025: 11 · 2024: 11 · 2023: 8 · 2022: 3
- FWCI
- 2.54
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- Ministry of Education, Culture, Sports, Science and Technology (17H00867)
- Japan Society for the Promotion of Science (17H00867, 894030)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- federated learning, multi-task learning, pancreas segmentation, pancreatic tumor segmentation, data heterogeneity, abdominal CT, medical image segmentation, FedAvg, cross-institutional data sharing
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