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PulseFlow is introduced, a source-conditioned counterfactual generation framework that combines conditional representation learning with invertible latent transport to edit cardiac rhythm while retaining information from the source.
Heterogeneous cardiac signals can mutually supervise a single foundation model of cardiac physiology using a physiology-aware joint-embedding predictive architecture built on a physiology-aware joint-embedding predictive architecture.
This work proposes a framework that aligns self-supervised respiratory encoders with medical terminology in a shared latent space turning them into a zero-shot-capable foundation model, and uses a medical LLM to synthesize structured reports from metadata, creating dense semantic anchors for contrastive learning.
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