VCDP: Variation-Conditioned Distributional Proxy Learning for Semi-Supervised Medical Image Segmentation

Unifying Active Learning and Semi-Supervised Learning for Medical Image Segmentation

RegAL is proposed, a unified active semi-supervised framework governed by a shared topology-aware Pareto optimization that couples sample acquisition with unlabeled data utilization and consistently outperforms state-of-the-art AL, SSL, and active semi-supervised baselines across Dice and boundary-distance metrics under extreme annotation scarcity.

Robust unsupervised domain adaptation for medical image segmentation via frequency-conditioned graph diffusion

Cross-domain variability in medical imaging, arising from differences in scanners, acquisition protocols, and patient populations, remains a major challenge for reliable semantic segmentation. Existing unsupervised domain adaptation (UDA) methods predominantly rely on image-level transformations or feature alignment, which often fail to preserve anatomical consistency under large domain shifts. In this work, we propose a novel structured latent UDA framework that performs domain alignment in a topology-aware representation space rather than directly modifying image appearance. Specifically, we introduce a Frequency-Conditioned Graph Diffusion paradigm, where convolutional features are transformed into anatomical graphs to explicitly capture structural relationships. A latent diffusion process then progressively refines these graph embeddings, guided by frequency-aware contextual cues, enabling robust cross-domain alignment. To further enhance generalization, we integrate structural consistency regularization with adversarial latent alignment, eliminating the need for labeled target data. A dedicated decoder reconstructs dense segmentation maps, while stochastic diffusion sampling provides uncertainty estimates for improved potential clinical reliability. Extensive experiments on multiple public medical imaging benchmarks demonstrate that our method consistently outperforms state-of-the-art UDA approaches, achieving superior segmentation accuracy and robustness under significant domain shifts. These results highlight the effectiveness of structured latent modeling and diffusion-based learning for robust domain-adaptive segmentation.

Distribution-Constrained Semi-Supervised Learning for Medical Organ Segmentation

Semi-supervised Medical Image Segmentation via Perturbation-Aware Mutual Learning and Edge-Aware Uncertainty Loss for Accurate Anatomical Delineation.

This paper proposes a novel framework that effectively leverages unlabeled data to improve segmentation performance in cardiac structures and applies a novel consistency constraint by a dual fine-grained boundary loss that provide global characteristics-based guidance from the transition of the boundary region and an edge-aware uncertainty loss.

Segmentation-synthesis co-training for semi-supervised domain generalizable medical image segmentation.

Semi-supervised domain generalization (SSDG) faces two fundamental challenges that hinder model generalizability: label scarcity and domain shifts. Recent studies tackle this challenging task by building on a scheme that integrates the strong-weak pseudo supervision paradigm with specific data augmentation strategies. However, semantic inconsistencies between style-augmented unlabeled images and their pseudo labels limit the effectiveness of this scheme and impair the generalizability of trained models. One critical question arises: How to ensure semantic consistency and style diversity of unlabeled-image and pseudo-label pairs for training a well-generalized model? To this end, we introduce ReMatch, a segmentation-synthesis co-training framework for semi-supervised domain generalization in medical image segmentation. The core of ReMatch lies in the SynTS algorithm that Synthesizes unlabeled images with both high semantic consistency and style diversity by leveraging Texture and Shape features derived from the segmentation process. Extensive experiments on single-source single-target and single-source multi-target cross-domain settings with various image modalities demonstrate that ReMatch offers an effective solution for SSDG, achieving compelling performance. For example, compared with the state-of-the-art based on the aforementioned scheme, ReMatch achieves average improvements of 2.31% and 1.68% in Dice similarity coefficients under the two cross-domain settings with 10% labeled data, respectively. Code is available at https://github.com/Senyh/ReMatch.

Asymmetric dual-path structural consistency for semi-supervised medical image segmentation

Deep neural networks have achieved remarkable progress in medical image segmentation, but their performance still depends heavily on large-scale pixel-level annotations. Semi-supervised learning (SSL) alleviates this burden by leveraging unlabeled data, yet conventional teacher–student frameworks often suffer from error accumulation due to unidirectional supervision and noisy pseudo-labels. To overcome these limitations, we propose an Asymmetric Dual-Path Mutual Supervision (BiAsy-MS) framework that enables two models to collaborate through structurally and contextually diverse views. We introduce an irregular region-mixing augmentation (BezierMix) that generates anatomically aligned masks and asymmetric semantic contexts, promoting the exchange of complementary structural priors across labeled and unlabeled domains. In addition, an Information-driven Pseudo-Label Weighted mechanism adaptively emphasizes reliable pseudo-labels by accounting for information value, prediction stability, and confidence distribution, thereby suppressing noise and improving boundary recognition. Extensive experiments on three benchmark datasets (LA, Pancreas-NIH, and ACDC) show that our method consistently surpasses state-of-the-art SSL approaches under limited supervision (5% and 10% labeled data), achieving superior robustness, generalization, and data efficiency.