UPL-SFDA: Uncertainty-Aware Pseudo Label Guided Source-Free Domain Adaptation for Medical Image Segmentation-Reference-Cited by-同舟云学术

UPL-SFDA: Uncertainty-Aware Pseudo Label Guided Source-Free Domain Adaptation for Medical Image Segmentation

Published:2023-12 Issue:12 Volume:42 Page:3932-3943
ISSN:0278-0062
Container-title:IEEE Transactions on Medical Imaging
language:
Short-container-title:IEEE Trans. Med. Imaging

Author:

Wu Jianghao¹^ORCID,Wang Guotai¹^ORCID,Gu Ran¹^ORCID,Lu Tao²,Chen Yinan³,Zhu Wentao⁴^ORCID,Vercauteren Tom⁵^ORCID,Ourselin Sébastien⁵^ORCID,Zhang Shaoting¹

Affiliation:

1. School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China

2. Department of Radiology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China

3. SenseTime Research, Shanghai, China

4. Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou, China

5. School of Biomedical Engineering & Imaging Sciences, King’s College London, London, U.K

Funder

National Natural Science Foundation of China

Sichuan Province International Science, Technology and Innovation Cooperation Foundation

Wellcome/Engineering and Physical Sciences Research Council

Medtronic/Royal Academy of Engineering (R.A.Eng). Research Chair

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Radiological and Ultrasound Technology,Software

Link

http://xplorestaging.ieee.org/ielx7/42/10336247/10261231.pdf?arnumber=10261231

Reference46 articles.

1. U-Net: Convolutional networks for biomedical image segmentation;ronneberger;Proc MICCAI,2015

2. Efficient test-time model adaptation without forgetting;niu;arXiv 2204 02610,2022

3. Test-Time Unsupervised Domain Adaptation

4. Tent: Fully test-time adaptation by entropy minimization;wang;Proc ICLR,2021

5. Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision