Beyond Training from Scratch: Foundation Models for Data-Efficient and Generalizable Cardiac MRI Reconstruction
cs.CV
Submitted: 2026-10-06
Updated: 2026-10-06
Comments: Accepted at ECCVW 2026
Code: https://github.com/Hashmi360/BeyondScratch-CMR
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- One-shot Localization and Segmentation of Medical Images with Foundation Models
- Layer Normalization
- Evaluating General Purpose Vision Foundation Models for Medical Image Analysis: An Experimental Study of DINOv2 on Radiology Benchmarks
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
- Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity
- Reference-based Magnetic Resonance Image Reconstruction Using Texture Transformer
- LORAKI: Autocalibrated Recurrent Neural Networks for Autoregressive MRI Reconstruction in k-Space
- Less Could Be Better: Parameter-efficient Fine-tuning Advances Medical Vision Foundation Models
- DINOv2: Learning Robust Visual Features without Supervision
- CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction
- fastMRI: An Open Dataset and Benchmarks for Accelerated MRI
- BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs
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