Exploring Generalizable Distillation for Efficient Medical Image Segmentation-Reference-Cited by-同舟云学术

Exploring Generalizable Distillation for Efficient Medical Image Segmentation

Published:2024-07 Issue:7 Volume:28 Page:4170-4183
ISSN:2168-2194
Container-title:IEEE Journal of Biomedical and Health Informatics
language:
Short-container-title:IEEE J. Biomed. Health Inform.

Author:

Qi Xingqun¹^ORCID,Wu Zhuojie²^ORCID,Zou Wenxuan³^ORCID,Ren Min⁴^ORCID,Gao Yifan⁵^ORCID,Sun Muyi⁶^ORCID,Zhang Shanghang⁷^ORCID,Shan Caifeng⁸^ORCID,Sun Zhenan⁹^ORCID

Affiliation:

1. AIS, The Hong Kong University of Science and Technology, Hong Kong, China

2. School of EECS, The University of Queensland, Brisbane, QLD, Australia

3. School of BCME, Beihang University, Beijing, China

4. School of AI, Beijing Normal University, Beijing, China

5. School of MIS, Guangzhou University, Guangzhou, China

6. School of AI, Beijing University of Posts and Telecommunications, Beijing, China

7. School of CS, Peking University, Beijing, China

8. State Key Laboratory for Novel Software Technology and School of Intelligence Science and Technology, Nanjing University, Nanjing, China

9. CRIPAC, MAIS, CASIA, Beijing, China

Funder

National Science and Technology Major Project of China

National Natural Science Foundation of China

Clinical Research Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6221020/10582272/10491241.pdf?arnumber=10491241

Reference69 articles.

1. U-Net: Convolutional Networks for Biomedical Image Segmentation

2. UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation

3. V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation

4. A Novel Negative-Transfer-Resistant Fuzzy Clustering Model with a Shared Cross-Domain Transfer Latent Space and its Application to Brain CT Image Segmentation

5. Adapting Off-the-Shelf Source Segmenter for Target Medical Image Segmentation