Cross-modality cardiac image segmentation model that addresses spatial-temporal confounding -- where the anatomy and imaging-modality elements of cardiac images are intertwined across space and time. DCL performs multi-dimensional causal intervention, modeling causal relationships between images and labels as well as causality along the time and space dimensions, integrating historical optimal interventions to transfer knowledge across temporal contexts. A diffusion mechanism further keeps extracted anatomical elements causally invariant across modalities. On cross-modality cardiac images (MR, CT, and ultrasound), DCL achieved a mean Dice of 0.951, outperforming other advanced segmentation methods.

Multimodal cardiac MRI foundation model that fuses 3D+T cine CMR (short-axis and long-axis views) with tabular patient health records (demographics, metabolic, and lifestyle factors) from 42,000 UK Biobank participants. Two-stage self-supervised pretraining -- masked-image reconstruction, then imaging-tabular contrastive alignment -- produces representations that transfer to whole-heart segmentation, cardiac phenotype/physiological-feature regression, and cardiac/metabolic disease classification within one unified framework.