Source-free domain transfer algorithm with reduced style sensitivity for medical image segmentation.
In unsupervised transfer learning for medical image segmentation, where existing algorithms face the challenge of error propagation due to inaccessible source domain data. In response to this scenario, source-free domain transfer algorithm with reduced style sensitivity (SFDT-RSS) is designed. SFDT-...
Saved in:
Main Authors: | Jian Lin, Xiaomin Yu, Zhengxian Wang, Chaoqiong Ma |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2024-01-01
|
Series: | PLoS ONE |
Online Access: | https://doi.org/10.1371/journal.pone.0309118 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
VP-SFDA: Visual Prompt Source-Free Domain Adaptation for Cross-Modal Medical Image
by: Yixin Chen, et al.
Published: (2025-01-01) -
Segment anything model for few-shot medical image segmentation with domain tuning
by: Weili Shi, et al.
Published: (2024-11-01) -
Medical image segmentation based on frequency domain decomposition SVD linear attention
by: Liu Qiong, et al.
Published: (2025-01-01) -
Reducing Cross-Sensor Domain Gaps in Tactile Sensing via Few-Sample-Driven Style-to-Content Unsupervised Domain Adaptation
by: Xingshuo Jing, et al.
Published: (2025-01-01) -
Medical Images Segmentation Based on Unsupervised Algorithms: A Review
by: Revella E. A. Armya, et al.
Published: (2021-04-01)