MetaCLIP-CMR applies CLIP-style contrastive learning to cardiac MRI by treating acquisition metadata as text labels, delivering 86.8% modality and 86.5% view accuracy plus top Dice scores on ACDC/M&Ms segmentation with far less pre-training data than recent large-scale CMR models.
Radiology: Artificial Intelligence7(2), e240443 (2025)
2 Pith papers cite this work. Polarity classification is still indexing.
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MoE-dqINR factorizes INR-based MRI reconstruction into shared spatial experts plus state-conditioned routing to unify dynamic and quantitative reconstruction at roughly 30 seconds per scan.
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MoE-dqINR: A Unified Mixture-of-Experts Implicit Neural Representation Framework for Scan-Specific Dynamic and Quantitative MRI Reconstruction
MoE-dqINR factorizes INR-based MRI reconstruction into shared spatial experts plus state-conditioned routing to unify dynamic and quantitative reconstruction at roughly 30 seconds per scan.