Vision foundation models have demonstrated strong generalization in medical image segmentation by leveraging large-scale, heterogeneous pretraining. However, they often struggle to generalize to specialized clinical tasks under limited annotations or rare pathological variations, due to a mismatch between general priors and task-specific requirements. To address this, we propose Uncertainty-informed Collaborative Learning (UnCoL), a dual-teacher framework that harmonizes generalization and specialization in semi-supervised medical image segmentation. Specifically, UnCoL distills both visual and semantic representations from a frozen foundation model to transfer general knowledge, while concurrently maintaining a progressively adapting teacher to capture fine-grained and task-specific representations. To balance guidance from both teachers, pseudo-label learning in UnCoL is adaptively regulated by predictive uncertainty, which selectively suppresses unreliable supervision and stabilizes learning in ambiguous regions. Experiments on diverse 2D and 3D benchmarks show that UnCoL consistently outperforms existing methods across most datasets and metrics, while achieving comparable performance in only a few cases. Moreover, our model delivers near fully supervised performance with markedly reduced annotation requirements. Code is available at: https://github.com/VivienLu/UnCoL.

Semi-supervised segmentation is extensively employed in medical image analysis due to its ability to leverage a small amount of labeled data alongside abundant unlabeled data. However, its performance is hindered by the inadequate knowledge of the data domain learned from limited labeled data and the absence of effective strategies for exploiting unlabeled regions, especially when annotations are extremely scarce. To address these challenges, the Segment Anything Model (SAM) has emerged as a promising solution. As a foundation model enriched by extensive and diverse domain knowledge, SAM has been leveraged to mitigate the epistemic uncertainty (EU) of semi-supervised segmentation models, while aleatoric uncertainty (AU) is often ignored. In this paper, we propose a novel semi-supervised medical image segmentation framework called UP-SAM, which adapts SAM for dual uncertainty assessments. The framework achieves effective collaboration between large foundation models and domain-specific models, leading to a simultaneous reduction in the impact of EU and AU. The experiments on the left atrium and pancreas datasets demonstrate the superior efficacy of UP-SAM against baseline methods. Particularly, UP-SAM exhibits substantial advantages over other semi-supervised learning models when dealing with exceedingly scarce labeled data. Code is available at https://github.com/VivienLu/UP-SAM.

Abstract Although current attention‐based speech enhancement methods have been proven to be capable of significantly improving the noise reduction performance, a bottleneck has arisen in juggling both detailed features and high‐level features: The more attention paid to such performance indicators as speech intelligibility index and articulation index will lead to the loss of subtle features such as syllable continuity and timbre distortion. To tackle such a problem, we divide the speech enhancement model into multiple stages, with a special attention mechanism introduced in each stage so that the detailed speech information is retained and the features of high levels are captured. In this research, a Multi‐Stage Attention Network (MSANet) is implemented in cascade by using three different attention modules: Self Attention, Channel Attention and Spatial Attention. The attention module in each stage needs only to focus on the features of its corresponding stage, thus giving full play to their respective advantages without affecting or damaging the features of other stages, helping decouple the feature extraction process and obtaining the features capable of complementing one another. The comparisons and ablation experiments show that the performance of MSANet is superior to those of current mainstream time domain or time‐frequency domain state‐of‐the‐art methods. Compared to the baseline, PESQ score of their model (3.11) has increased by 5%, CSIG (4.44) has increased by 2.5%, CBAK (3.63) has increased by 2.8% and COVL (3.81) has increased by 3.8%, which demonstrates the potential of MSANet as speech feature extraction backbones. An implementation of the PyTorch version is available here: http://www.msp‐lab.cn:1436/msp/MSANet .

A Modified FKCL (MFKCL) algorithm for automatic segmentation of MR brain images is proposed in this paper. This algorithm is an extension of traditional fuzzy Kohonen's competitive learning algorithm. In our method, a factor that can estimate the effect of the neighbor pixels to the central pixel is introduced into the objective function of the standard FKCL algorithm as the local information. The local information is applied to trail off the effect of noise to the result of MRI segmentation. Experiments with simulated MR data and real MR data show that our algorithm can resist not only the little, but also the heavy noise compared with standard FKCL segmentation and other reported methods.