most citedCross-supervised Dual Classifiers for Semi-supervised Medical Image Segmentation

2 citations · 4 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2024

Multi-scale Frequency Enhancement Network for Blind Image Deblurring

Yawen Xiang, Heng Zhou, Chengyang Li +2

Image deblurring is an essential image preprocessing technique, aiming to recover clear and detailed images form blurry ones. However, existing algorithms often fail to effectively…

cs.CV2024

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…

stat.ME20231 cited

Statistical and Practical Considerations in Planning and Conduct of Dose Optimization Trials

Ying Yuan, Heng Zhou, Suyu Liu

The US Food and Drug Administration launched Project Optimus with the aim of shifting the paradigm of dose-finding and selection towards identifying the optimal biological dose tha…

cs.CV20232 cited

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…

cs.CV20231 cited

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…