309 citations · 343 across the 28 of their papers we have counts for
22 papers · 1 filter
Disentangling Progress in Medical Image Registration: Beyond Trend-Driven Architectures towards Domain-Specific Strategies
Bailiang Jian, Jiazhen Pan, Rohit Jena +5
Medical image registration drives quantitative analysis across organs, modalities, and patient populations. Recent deep learning methods often combine low-level "trend-driven" comp…
BraTS orchestrator : Democratizing and Disseminating state-of-the-art brain tumor image analysis
Florian Kofler, Marcel Rosier, Mehdi Astaraki +34
The Brain Tumor Segmentation (BraTS) cluster of challenges has significantly advanced brain tumor image analysis by providing large, curated datasets and addressing clinically rele…
Semi-Supervised Medical Image Segmentation via Knowledge Mining from Large Models
Yuchen Mao, Hongwei Li, Yinyi Lai +4
Large-scale vision models like SAM have extensive visual knowledge, yet their general nature and computational demands limit their use in specialized tasks like medical image segme…
Unsupervised Low-dose CT Reconstruction with One-way Conditional Normalizing Flows
Ran An, Ke Chen, Hongwei Li
Deep-learning methods have shown promising performance for low-dose computed tomography (LDCT) reconstruction. However, supervised methods face the problem of lacking labeled data…
TV-based Deep 3D Self Super-Resolution for fMRI
Fernando Pérez-Bueno, Hongwei Bran Li, Matthew S. Rosen +3
While functional Magnetic Resonance Imaging (fMRI) offers valuable insights into cognitive processes, its inherent spatial limitations pose challenges for detailed analysis of the…
A Low-dose CT Reconstruction Network Based on TV-regularized OSEM Algorithm
Ran An, Yinghui Zhang, Xi Chen +3
Low-dose computed tomography (LDCT) offers significant advantages in reducing the potential harm to human bodies. However, reducing the X-ray dose in CT scanning often leads to sev…