arXiv Computer Vision
Diffusion-Based Tumor Inpainting for Renal Segmentation under Clinical Data Scarcity
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arXiv AI
Jun 9
Robust Renal Mass Segmentation on CT: A Validation Study of an AI-Based Framework
arXiv:2505. 07573v2 Announce Type: replace-cross Abstract: Renal mass segmentation has important potential to enhance the clinical workflow, especially in settings requiring quantitative assessments.
arXiv AI
Jun 10
++nnU-Net: Scaling nnU-Net with Prefix-Based Data Augmentation
arXiv:2606. 10713v1 Announce Type: cross Abstract: The nnU-Net has demonstrated continuous success in medical segmentation tasks, which heavily rely on the availability and diversity of annotated biomedical data.
arXiv Computer Vision
Sep 14
3D CT-to-PET Translation via Latent Brownian Bridge Diffusion
arXiv:2609.12860v1 Announce Type: new Abstract: Computed tomography (CT) and positron emission tomography (PET) provide complementary anatomical and functional information for cancer diagnosis and tr...
arXiv Machine Learning
Aug 27
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.
Hugging Face Trending Papers
Aug 26
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 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.