HEART-JEPA: LEARNING CROSS-VIEW REPRESEN- TATIONS FOR CARDIAC MRI WITH JOINT-EMBEDDING PREDICTIVE ARCHITECTURES

Cine cardiac magnetic resonance imaging (CMR) captures the same heart from complementary anatomical views. Learning representations that transfer between these views is a central challenge for reusable cardiac prediction. We introduce HEART-JEPA, a cross-view joint-embedding predictive architecture that predicts masked latent features across anatomical planes without spatially matched patches. Pretrained on 39,992 UK Biobank participants, HEART-JEPA achieves a crossview structural-transfer R2 of 0.48 in 15,935 participants absent from pretraining, without target-view refitting or recalibration. Matched ablation studies show substantial gains over same-view pretraining and stronger cardiac phenotype prediction and structural and functional transfer than cross-view contrastive learning. In a separate ventricular-function benchmark, supervised linear probes fitted to concatenated frozen features from all three views achieve the lowest mean LVEF MAE among the seven evaluated models (3.19 percentage points), with competitive RVEF performance. These findings establish cross-view latent prediction as an effective approach to cardiac representation learning and motivate its extension to videos acquired from complementary, spatially unaligned views