arXiv Computer Vision
O2D-MedNet: An Optimization-Aware Dual-Domain Framework for Robust Medical Image Segmentation
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv AI
Aug 18
EA-LiteUNet: An Edge-Adaptive and Resource-Efficient U-Net for Boundary-Sensitive Dermoscopic Image Segmentation
arXiv:2608. 15537v1 Announce Type: cross Abstract: Accurate boundary delineation remains a persistent challenge in dermoscopic image segmentation because of blurred lesion margins, heterogeneous textures, and complex background artifacts.
arXiv Computer Vision
Sep 7
CoMLP: Cooperatively-Gated MLPs for Fine-Grained Cross-Modal Information Fusion in Medical Image Segmentation
CoMLP introduces a cooperatively-gated MLP module that fuses multimodal medical data—such as imaging modalities and clinical reports—without relying on computationally heavy cross-attention. The module uses regional and dilated MLP interactions to capture both local and global cross-modal dependencies, enabling fine-grained fusion at high spatial resolutions. Experiments on five segmentation benchmarks, covering 2D/3D images and diverse anatomical regions, show consistent improvements over state-of-the-art multi-modal and language-guided methods, highlighting the effectiveness of MLP-based interaction for medical image segmentation.
arXiv Computer Vision
Sep 10
When Fusion Fails: Corruption-Aware Rebalanced Fusion for Multi-Modal Medical Image Segmentation
arXiv:2609.10261v1 Announce Type: new Abstract: Multi-modal medical image segmentation leverages complementary diagnostic information, yet fusion can underperform single-modality baselines when spati...
Hugging Face Trending Papers
Jun 29
HTC-SGA Former: A Hybrid Transformer-CNN Network with Self-Guided Attention and a New Boundary-Weighted Adaptive Loss for Coronary DSA Vessel Segmentation
Accurate coronary Digital Subtraction Angiography (DSA) vessel segmentation is essential for computer-aided diagnosis and treatment planning of coronary artery disease (CAD). However, thin low-contrast vessels, background interference, and severe vessel-background class imbalance make reliable segmentation of weak distal branches and vessel boundaries challenging.
arXiv AI
Jun 9
AMN: An Adaptive Multi-Scale Fusion Network with Boundary and Uncertainty Modeling for Nuclei Segmentation
arXiv:2606. 07633v1 Announce Type: cross Abstract: Accurate classification of nuclei subtypes in histopathology images is critical for downstream tasks including tumor grading, immune infiltrate quantification, and prognosis prediction.
Hugging Face Trending Papers
Aug 10
CoInS-Net: A Continuous Position-Aware Network for Joint Medical Image Interpolation and Segmentation
Accurate medical image interpolation and anatomical structure segmentation are fundamental for computer-aided diagnosis and treatment planning. Anisotropic medical volumes with sparse through-plane sampling often suffer from structural discontinuity and boundary blur, hindering reliable clinical image analysis.