HyWaveDiff: Reliability-Guided Diffusion for Semi-Supervised Medical Image Segmentation

Reliable pseudo-labeling remains a central challenge in semi-supervised medical image segmentation, particularly near low-contrast and irregular anatomical boundaries. We propose HyWaveDiff, a reliability-guided mask-diffusion framework that converts stochastic diffusion variation into actionable supervision for unlabeled data. An EMA teacher generates multiple predictions and feature embeddings, from which Multi-Cue Reliability Refinement (MRR) integrates prediction confidence, stochastic consistency, geometry-dependent feature dispersion, and image-derived boundary evidence. The resulting reliability is used to correct soft pseudo-labels and assign continuous pixel-wise supervision weights. A Poincaré-ball dispersion models feature disagreement, while multi-scale Haar-wavelet guidance supplies frequency-specific structural information to the denoiser. Reliability-adaptive optimization and frequency-oriented consistency further regulate unlabeled learning. With only three labeled training cases per dataset, HyWaveDiff achieves Dice scores of 82.38% on PROMISE12 MRI and 88.33% on Task09 Spleen CT, obtaining the highest observed Dice among the evaluated semi-supervised methods. On Task09 Spleen, it also achieves the lowest HD95 and ASD among the evaluated semi-supervised methods. These results support reliability-guided use of stochastic diffusion predictions for low-annotation medical image segmentation.