Source-Free Domain Adaptation for Cross-Modality Abdominal Multi-Organ Segmentation Challenges
Abdominal organ segmentation in CT images is crucial for accurate diagnosis, treatment planning, and condition monitoring. However, the annotation process is often hindered by challenges such as low contrast, artifacts, and complex organ structures. While unsupervised domain adaptation (UDA) has sho...
Saved in:
Main Authors: | Xiyu Zhang, Xu Chen, Yang Wang, Dongliang Liu, Yifeng Hong |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2025-05-01
|
Series: | Information |
Subjects: | |
Online Access: | https://www.mdpi.com/2078-2489/16/6/460 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A survey of unsupervised domain adaptive semantic segmentation algorithms based on deep learning
by: Ying Junjie, et al.
Published: (2024-01-01) -
Cross-Receiver Radio Frequency Fingerprint Identification: A Source-Free Adaptation Approach
by: Jian Yang, et al.
Published: (2025-07-01) -
Block-Wise Domain Adaptation for Workload Prediction from fNIRS Data
by: Jiyang Wang, et al.
Published: (2025-06-01) -
Elevation-Aware Domain Adaptation for Sematic Segmentation of Aerial Images
by: Zihao Sun, et al.
Published: (2025-07-01) -
BucketAugment: Reinforced Domain Generalisation in Abdominal CT Segmentation
by: David Jozef Hresko, et al.
Published: (2024-01-01)