[A semi-supervised MRI image segmentation dual-network model for regions with ambiguous boundaries and heterogeneous regions].

Uncertainty-guided decoupled complementary network for binary semi-supervised medical image segmentation

Semi-supervised medical image segmentation methods have drawn wide attention as they reduce reliance on heavily annotated data. However, existing models suffer from confirmation bias with limited annotations, and structural or parameter coupling hinders self-correction, especially for medical images with ambiguous boun...

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

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 is proposed.

Dual-Domain Structural Guidance with Grayscale-Enhanced Edge Learning for Robust Medical Image Segmentation.

MCSD-net: a heterogeneous dual-student network for semi-supervised medical image segmentation

MCSD-Net is proposed, a heterogeneous dual-student framework that pairs a structure-oriented Mamba student with a detail-oriented ConvNeXt student and its Cross-Scale Selective Fusion (CS2F) module uses input-dependent gates to combine aligned decoder features and branch logits.

Dynamic adaptation and boundary-aware learning for semi-supervised medical image segmentation.

DABAL is proposed, a semi-supervised framework designed to improve both supervision reliability and contour localization and introduces a Dynamic-static Domain Adaptive Adapter (DDAA) into the Segment Anything Model (SAM) encoder to preserve stable structural priors while providing input-dependent feature compensation...

DMFNet: a semi-supervised dual-path multiscale fusion network for lesion segmentation in breast ultrasound

Accurate lesion segmentation in breast ultrasound images is crucial for early diagnosis and treatment planning, yet remains highly challenging due to inherent imaging characteristics, including low signal-to-noise ratio, speckle noise artifacts, blurred boundaries, and significant inter-lesion appearance variability. F...