1 citations · 1 across the 4 of their papers we have counts for
4 papers
TransMedSeg: A Transferable Semantic Framework for Semi-Supervised Medical Image Segmentation
Mengzhu Wang, Jiao Li, Shanshan Wang +4
Semi-supervised learning (SSL) has achieved significant progress in medical image segmentation (SSMIS) through effective utilization of limited labeled data. While current SSL meth…
AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation
Houcheng Su, Mengzhu Wang, Jiao Li +3
In semi-supervised domain adaptation (SSDA), the model aims to leverage partially labeled target domain data along with a large amount of labeled source domain data to enhance its…
GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation
Mengzhu Wang, Jiao Li, Houcheng Su +3
Semi-supervised learning (SSL) has made notable advancements in medical image segmentation (MIS), particularly in scenarios with limited labeled data and significantly enhancing da…
Open-World Test-Time Training: Self-Training with Contrast Learning
Houcheng Su, Mengzhu Wang, Jiao Li +3
Traditional test-time training (TTT) methods, while addressing domain shifts, often assume a consistent class set, limiting their applicability in real-world scenarios characterize…