arXiv Computer Vision
LHMCF-Net: A Learned Hyperbolic Mean Curvature Flow Network for Medical Images Segmentation
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arXiv Machine Learning
Aug 21
Flow Matching Meets 3D Curvilinear Structure Segmentation in Medical Imaging
arXiv:2608. 19965v1 Announce Type: cross Abstract: Segmentation of curvilinear anatomical structures in 3D medical images remains challenging due to complex topology, severe class imbalance, weak contrast, and large variations in structure morphology.
arXiv AI
Aug 10
H2AL: Hyperbolic Hierarchy-aware Aggregative Learning for Registration-based Few-shot Medical Image Segmentation
arXiv:2608. 07340v1 Announce Type: cross Abstract: Registration-based Few-shot medical image segmentation (RFMIS) aims to generate pseudo-labels for unlabeled images by warping a labeled image through registration.
arXiv Machine Learning
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Unsupervised Anatomical Feature Learning via Diffusion Models: Enhanced Medical Image Segmentation with Denoising Diffusion Probabilistic Models
The paper introduces an unsupervised approach to medical image segmentation by training a Denoising Diffusion Probabilistic Model (DDPM) on 21 unlabeled abdominal CT scans to learn anatomical features. The encoder weights from the DDPM are transferred to a U‑Net for downstream segmentation on the BTCV multi‑organ dataset, resulting in a significant Dice score improvement for liver segmentation from 0.75 to 0.93. In low‑data regimes, diffusion‑pretrained models retain robust performance, achieving high Dice scores even with only 10% of labeled data.
arXiv Machine Learning
Jun 16
Learning a Sampling-Free Variational DNN Plugin from Tiny Training Sets to Refine OOD Segmentation With Uncertainty Estimation
arXiv:2606. 15837v1 Announce Type: cross Abstract: Deep neural networks (DNNs) frequently fail to generalize to out-of-distribution (OOD) medical images because of variations in scanners and acquisition protocols.
Hugging Face Trending Papers
6d ago
Unsupervised Anatomical Feature Learning via Diffusion Models: Enhanced Medical Image Segmentation with Denoising Diffusion Probabilistic Models
Acquiring pixel-level annotations for medical image segmentation is a severe bottleneck. Traditional U-Net architectures, while effective, learn local texture patterns and lack awareness of global ana...
arXiv Computer Vision
Aug 25
SymmAdapt: Symmetrical Flow Matching for Source-Free Domain Adaptation in Medical Image Segmentation
arXiv:2608.22532v1 Announce Type: new Abstract: Domain shift across imaging modalities and acquisition sites remains a significant barrier to the clinical deployment of segmentation models. Source-fr...