Model-Heterogeneous Semi-Supervised Federated Learning for Medical Image Segmentation-Reference-Cited by-同舟云学术

Model-Heterogeneous Semi-Supervised Federated Learning for Medical Image Segmentation

Published:2024-05 Issue:5 Volume:43 Page:1804-1815
ISSN:0278-0062
Container-title:IEEE Transactions on Medical Imaging
language:
Short-container-title:IEEE Trans. Med. Imaging

Author:

Ma Yuxi¹^ORCID,Wang Jiacheng²^ORCID,Yang Jing¹,Wang Liansheng³^ORCID

Affiliation:

1. National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China

2. Department of Computer Science, School of Informatics, Xiamen University, Xiamen, China

3. National Institute for Data Science in Health and Medicine, and the Department of Computer Science, School of Informatics, Xiamen University, Xiamen, China

Funder

Ministry of Science and Technology of the People’s Republic of China

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/42/10517791/10379169.pdf?arnumber=10379169

Reference50 articles.

1. XBound-Former: Toward Cross-Scale Boundary Modeling in Transformers

2. Boundary-Aware Transformers for Skin Lesion Segmentation

3. Combining Self-training and Hybrid Architecture for Semi-supervised Abdominal Organ Segmentation

4. Efficient Global-Local Memory for Real-Time Instrument Segmentation of Robotic Surgical Video

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