The paper presents a semi‑supervised biomedical image segmentation method that uses a diffusion‑based teacher–student framework. The teacher is pretrained via unsupervised diffusion reconstruction and then co‑trained with a student, leveraging supervised labels and cross pseudo‑supervision on unlabeled data. A multi‑round extension generates multiple stochastic reconstructions to further refine pseudo‑labels, achieving competitive or superior results on several 2D and 3D biomedical datasets, especially when labels are scarce.
By Luca Ciampi, Gabriele Lagani, Giuseppe Amato, Fabrizio Falchi
arXiv:2609.14943v1 Announce Type: new
Abstract: Pseudo-labeling is a strong paradigm for semi-supervised medical image segmentation, yet its effectiveness is highly sensitive to confidence thresholdi...
By Hongyu Liu, Yinlong Wang, Lusha Li, Hui Meng
arXiv:2609.23815v1 Announce Type: new
Abstract: Medical image segmentation models typically rely on large amounts of densely annotated volumetric data, limiting their scalability across tasks and ima...
By Georgios Triantafyllou, Dimitris K. Iakovidis
The paper introduces an unsupervised approach to medical image segmentation by training a Denoising Diffusion Probabilistic Model (DDPM) on 21 unlabeled abdominal CT scans to learn anatomical features. The encoder weights from the DDPM are transferred to a U‑Net for downstream segmentation on the BTCV multi‑organ dataset, resulting in a significant Dice score improvement for liver segmentation from 0.75 to 0.93. In low‑data regimes, diffusion‑pretrained models retain robust performance, achieving high Dice scores even with only 10% of labeled data.
By Akshat G, Divyansh Gupta, Shaleen Bhatnagar, Shilpa Ankalaki, Tusar Kanti Mishra
The paper introduces a two‑stage framework for point‑supervised change detection that leverages SAM2 priors to generate object‑aware candidate masks from sparse point annotations. In Stage I, a mask selection strategy converts generic segmentation outputs into reliable change pseudo‑labels, followed by a lightweight CNN refinement module with an uncertainty‑aware loss to enhance boundary quality. Stage II employs a teacher‑student self‑training loop, where the teacher is updated via exponential moving average and periodically refreshes pseudo‑labels, creating a closed‑loop optimization that alternates between pseudo‑label refinement and model re‑optimization. Experiments on WHU‑CD, LEVIR‑CD, and SYSU‑CD show the method surpasses prior weakly supervised approaches and competes with several fully supervised methods.
Acquiring pixel-level annotations for medical image segmentation is a severe bottleneck. Traditional U-Net architectures, while effective, learn local texture patterns and lack awareness of global ana...