Bidirectional uncertainty-aware region learning for semi-supervised medical image segmentation.

Pseudo-label enhanced consistency learning for semi-supervised medical image 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.

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.

OaCMatch: Semi-supervised medical image segmentation network based on unlabeled information constraints and joint feature perturbation

Semi-supervised medical image segmentation via Cross Reliable Knowledge Distillation.

Semi-supervised medical image segmentation aims to alleviate the dependence on large-scale annotations by exploiting unlabeled data. However, existing methods often lack effective mechanisms for leveraging semantic knowledge from labeled data and enabling reliable knowledge interaction among unlabeled perturbation branches. To address this, we propose Cross Reliable Knowledge Distillation (CRKD), a semi-supervised framework for medical images. Specifically, we first introduce a Class Semantic Distillation (CSD) module, which transfers class-level semantic knowledge from labeled data to unlabeled representations through class prototype alignment in the feature space, facilitating discriminative feature learning. Then, we propose a Low-Entropy Consistency (LEC) module that dynamically emphasizes reliable low-entropy regions, improving pixel-level prediction consistency under diverse perturbations. Finally, we develop a Cross Knowledge Distillation (CKD) module, consisting of Cross Pixel Knowledge Distillation (CPKD) and Cross Feature Knowledge Distillation (CFKD), which enables reliability-aware bidirectional knowledge transfer in both feature and pixel spaces, allowing perturbation branches to collaboratively correct uncertain representations and predictions without introducing additional network architectures. Extensive experiments demonstrate the effectiveness of CRKD, achieving significant performance gains and robust generalization across diverse imaging modalities and lesion types. Code is available at https://github.com/eeaesa/CRKD.

Semi-supervised Prostate Multi-Regional Semantic Segmentation with Patch-Based Plug-and-Play Correction Guidance.

The high cost of medical image annotation severely restricts the clinical application of prostate precise multi-regional segmentation technologies. To address existing bottlenecks in semi-supervised learning methods, including insufficient alignment of local anatomical structures and pseudo-label noise accumulation, this paper proposes a labeled patch guidance (LPG) for patch-level interactive enhancement module that optimizes pseudo-label quality for unlabeled data by dynamically mining anatomical prior knowledge from labeled data. Specifically, 1) A labeled patch mutual correction (LPMC) mechanism first performs bidirectional exchange of annotated data across augmented states, obtaining newly labeled data with enhanced model robustness and establishing a high-confidence anatomical prior patch pool for prostate regions. 2) An unlabeled patch capture learning (UPCL) mechanism is then proposed to inject reliable anatomical information into low-confidence patches of unlabeled data through feature similarity retrieval from the high-confidence anatomical prior patch pool, thereby improving the model's ability to recognize feature distributions in unlabeled data. Comparative experiments demonstrate that our method significantly enhances the performance of mainstream semi-supervised medical image segmentation models on PROMISE12, MSD, HPH55, and ACDC datasets. In ablation studies, GradCAM-based interpretability analysis visually demonstrates that the LPG-equipped model effectively suppresses ambiguous boundaries in prostate segmentation and concentrates model attention on regions of interest. The source code associated with this work has been made publicly accessible on GitHub at https://github.com/hai-medicallab/LPG.