TL;DR
A novel semi-supervised medical image segmentation method incorporating global geometry attention and information propagation (GGIP) that achieves the state-of-the-art (SOTA) performance on a series of datasets, including the NIH pancreas, left atrium, and brain tumor datasets.
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...
This work introduces a Class Semantic Distillation 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, and develops a Low-Entropy Consistency module that dynamically em...
This work proposes a conditional Flow Matching (CFM) based framework for efficient 3D medical image segmentation that maintains competitive segmentation accuracy while achieving an inference time of only 1.14 seconds per volume, offering a clear efficiency advantage over existing generative segmentation methods.
Registration-based Few-shot medical image segmentation (RFMIS) aims to generate pseudo-labels for unlabeled images by warping a labeled image through registration. However, existing methods primarily perform pixel-level optimization and inference in Euclidean space, treating anatomical structures as flat and disjoint....
Using unlabeled images for diffusion-based pretraining successfully embeds robust anatomical features prior to human supervision, transforming U-Nets into anatomy-aware systems.
Accurate 3D medical image segmentation remains extremely challenging under severe anatomical variability caused by respiration motion, growth, and tumor-induced deformation. Existing prompt-based strategies mainly rely on visual and textual cues that essentially capture what organs look like or what they mean, al...
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