Semi-supervised semantic segmentation of cell nuclei with diffusion model and collaborative learning

The proposed DTSeg framework addresses cell nuclei segmentation under limited labeled data by integrating unsupervised LDM training on diverse unlabeled datasets. Collaborative learning demonstrated effectiveness in enhancing the generalization capability of DTSeg to achieve superior results across diverse datasets and cases. Furthermore, the method supports multi-channel inputs and demonstrates strong generalization to both in-distribution and OOD scenarios.