Hugging Face Trending Papers
TRiST: Topology-Reinforced Swin Transformer for Medical Image Analysis
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arXiv Computer Vision
22h ago
TRiST: Topology-Reinforced Swin Transformer for Medical Image Analysis
TRiST (Topology‑Reinforced Swin Transformer) augments hierarchical Vision Transformers with explicit topological information for medical image analysis. It computes 2D persistent homology (H0 and H1) at four Swin stages, refines persistence bands with gated residual networks, and injects learned key‑saliency scores into shifted‑window attention. The resulting Topo‑SwinV2‑B and Topo‑Swin‑UNet models outperform Swin baselines across five medical datasets.
arXiv Computer Vision
Oct 26
VTRL-Med: Progressive Visual Refinement with Topology-Guided Regularization for Medical Image Clustering
VTRL-Med is a progressive visual‑structural learning framework that incorporates topology‑guided regularization for medical image clustering. It adapts the Segment Anything Model to preserve structural cues, generates pseudo‑labels via prototype selection and k‑NN consistency, and refines visual features while an auxiliary topology branch—built from persistent homology descriptors—provides geometric regularization. The method achieves state‑of‑the‑art clustering accuracy, improving average performance by 4.48% over prior approaches and further boosting accuracy by 1.3–4.3% with topology guidance.
arXiv Computer Vision
Aug 25
CiUNet: A Hybrid Swin-CNN UNet for Medical Image Segmentation
arXiv:2608.22281v1 Announce Type: cross Abstract: Medical image segmentation requires high accuracy and robustness, yet practical commercial deployment also demands privacy preservation and computati...
arXiv AI
Jun 30
TopoAgent: An Agentic Framework for Automated Topology Learning in Medical Imaging
arXiv:2606. 29763v1 Announce Type: cross Abstract: Topological data analysis (TDA), particularly persistent homology (PH), captures geometric structural properties in medical images (e.
Hugging Face Trending Papers
Jul 1
MedCAGD: Context-Aware Gated Decoder for Efficient Medical Image Segmentation
Medical image segmentation relies on the ability of encoder-decoder architectures to translate rich feature representations into accurate pixel-level predictions under challenging conditions such as low contrast, structural ambiguity, and scale variability. While recent advances in large-scale pretraining and transformer-based encoders have substantially improved feature extraction, segmentation accuracy remains constrained by decoder design, particularly in terms of cross-scale alignment, contextual integration, and boundary preservation.
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.