11 citations · 15 across the 5 of their papers we have counts for
5 papers
An Evidential-enhanced Tri-Branch Consistency Learning Method for Semi-supervised Medical Image Segmentation
Zhenxi Zhang, Heng Zhou, Xiaoran Shi +3
Semi-supervised segmentation presents a promising approach for large-scale medical image analysis, effectively reducing annotation burdens while achieving comparable performance. T…
Multi Task Consistency Guided Source-Free Test-Time Domain Adaptation Medical Image Segmentation
Yanyu Ye, Zhenxi Zhang, Wei Wei +1
Source-free test-time adaptation for medical image segmentation aims to enhance the adaptability of segmentation models to diverse and previously unseen test sets of the target dom…
Cross-supervised Dual Classifiers for Semi-supervised Medical Image Segmentation
Zhenxi Zhang, Ran Ran, Chunna Tian +4
Semi-supervised medical image segmentation offers a promising solution for large-scale medical image analysis by significantly reducing the annotation burden while achieving compar…
Self-aware and Cross-sample Prototypical Learning for Semi-supervised Medical Image Segmentation
Zhenxi Zhang, Ran Ran, Chunna Tian +4
Consistency learning plays a crucial role in semi-supervised medical image segmentation as it enables the effective utilization of limited annotated data while leveraging the abund…
Don't Stop Learning: Towards Continual Learning for the CLIP Model
Yuxuan Ding, Lingqiao Liu, Chunna Tian +2
The Contrastive Language-Image Pre-training (CLIP) Model is a recently proposed large-scale pre-train model which attracts increasing attention in the computer vision community. Be…