6 papers
TeaMatch: Teachable Cross-Modal Representation Learning for 2D-3D Matching
Chongjian Wang, Junjie Gao
Learning reliable correspondences between images and point clouds is fundamental for 2D-3D matching. Despite recent progress in detection-free methods, existing approaches primaril…
SWINSleepNet: A Hierarchical Context-Aware Framework for Sleep Staging (v2)
Chongjian Wang, Junjie Gao
Automatic sleep staging is a critical role in sleep disorder diagnosis, sleep quality assessment, and long-term health monitoring; however, existing approaches suffer poor performa…
LGFNet: A CTC-Guided Local-Global Fusion Framework for Single-Channel Sleep Staging
Chongjian Wang, Zhenghang Hou, Junjie Gao +3
Sleep staging remains challenging due to long-range temporal dependencies, ambiguous stage transitions-particularly in N1-and substantial distribution shifts across subjects, sampl…
SHReg: Strictly Rotation-Equivariant Point Cloud Registration via Spherical Harmonics
Chongjian Wang, Junjie Gao
Point cloud registration critically depends on local features that are both distinctive and robust to arbitrary 3D rotations. Existing learning-based methods typically approximate…
A data- and compute-efficient chest X-ray foundation model beyond aggressive scaling
Chong Wang, Yabin Zhang, Yunhe Gao +9
Foundation models for medical imaging are typically pretrained on increasingly large datasets, following a "scale-at-all-costs" paradigm. However, this strategy faces two critical…
Dual Cross-image Semantic Consistency with Self-aware Pseudo Labeling for Semi-supervised Medical Image Segmentation
Han Wu, Chong Wang, Zhiming Cui
Semi-supervised learning has proven highly effective in tackling the challenge of limited labeled training data in medical image segmentation. In general, current approaches, which…