Chang-Jie Guan

Semi-Supervised Image Segmentation via Heterogeneous Dual-Teacher Mechanism and Multi-Directional Feature Modeling

This work proposes DTCL-Swin-UMamba, a novel Swin-UMamba backbone that fuses global context from Swin Transformer with linear-complexity long-range dependency learning from Mamba to address label scarcity and anisotropic structural distortion from unidirectional modeling in semi-supervised cardiac MRI segmentation.