1 citations · 1 across the 4 of their papers we have counts for
4 papers
On the Utility of Foundation Models for Fast MRI: Vision-Language-Guided Image Reconstruction
Ruimin Feng, Xingxin He, Ronald Mercer +2
Purpose: To investigate whether a vision-language foundation model can enhance undersampled MRI reconstruction by providing high-level contextual information beyond conventional pr…
OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation
Xingxin He, Aurora Rofena, Ruimin Feng +4
Magnetic Resonance Imaging (MRI) is indispensable in clinical practice but remains constrained by fragmented, multi-stage workflows encompassing acquisition, reconstruction, segmen…
Accelerating multiparametric quantitative MRI using self-supervised scan-specific implicit neural representation with model reinforcement
Ruimin Feng, Albert Jang, Xingxin He +1
Purpose: To develop a self-supervised scan-specific deep learning framework for reconstructing accelerated multiparametric quantitative MRI (qMRI). Methods: We propose REFINE-MORE…
Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation
Xingxin He, Yifan Hu, Zhaoye Zhou +2
Vision foundation models have achieved remarkable progress across various image analysis tasks. In the image segmentation task, foundation models like the Segment Anything Model (S…