HCT-Net: Coordinated Heterogeneous Co-Training for Semi-Supervised 3D Medical Image Segmentation

Heterogeneous learners can provide complementary training signals for semi-supervised segmentation, but their relative quality may change throughout training. We study this setting as a coordination problem involving three decisions: which learner provides supervision, when cross-branch learning is enabled, and how strongly each unlabeled sample contributes. We present HCT-Net, a heterogeneous Mamba–Transformer co-training framework in which smoothed labeled-batch performance serves as an online routing proxy. A relative-performance gate controls cross-branch activation, while training ramp-up and confidence coverage determine the strength of pseudo-label supervision. Prediction-level and feature-level transfer follow the same directed route with detached teacher targets. We evaluate HCT-Net on ACDC and LA under 10% and 15% labeled settings. At 15% labeling, HCT-Net achieves Dice scores of 83.01% and 85.00%, respectively, improving over symmetric cross-pseudo supervision using the same Mamba–Transformer pair by 1.09 and 2.20 percentage points. Across the evaluated backbone pairings, the largest observed CPS-to-HCT Dice improvements occur for the heterogeneous Mamba–Transformer pair on both datasets at 15% labeling. Epoch-wise diagnostics further characterize differences in branch diversity and error behavior across HCT configurations.