TL;DR
This paper proposes a semi-supervised medical image segmentation method based on pseudo-label enhanced consistency learning that uses unlabeled data to generate pseudo-labels and improves the model robustness and accuracy by enhancing the consistency of the pseudo-labels.
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...
ReMatch is introduced, a segmentation-synthesis co-training framework for semi-supervised domain generalization in medical image segmentation that Synthesizes unlabeled images with both high semantic consistency and style diversity by leveraging Texture and Shape features derived from the segmentation process.
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...
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.
Pseudo-labels can exploit unlabeled medical images, but high-overlap masks may still contain local contour errors and selected corrections may disappear during fine-tuning. We recast pseudo-label refinement as a repair-to-model problem and present Boundary-Selective Pseudo-Label Repair and Model Absorption (CPPA). A va...
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