EchoingECG

University of Toronto (McIntosh Lab)

12-lead ECG

Filter catalog by Modality:
ECG

Clinical text

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Text & EHR

Multimodal

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Multimodal

General Purpose / Multi-task

Filter catalog by Disease / Trait:
General / Foundation

Embedding

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Representation Learning

Hybrid

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Hybrid / Multi-branch

PyTorch

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PyTorch

CC BY-NC-ND 4.0

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Non-commercial / Research-only

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Distills knowledge from EchoCLIP, a vision-language echocardiography model, into ECG embeddings, aiming to improve how well ECG signals alone can predict echo-derived measures of cardiac function. Combines a 1D ECG encoder with a BioBERT text encoder under a probabilistic cross-modal embedding objective that captures uncertainty. Published at MICCAI 2025 by the University of Toronto's McIntosh Lab.

memory Specifications

gavel License

CC BY-NC-ND 4.0

check_small Open source close_small No commercial use close_small No modifications allowed Attribution required No share-alike requirement

License for model weights only. Associated code may be licensed seperately, check code source for specific terms.

description Publication

EchoingECG: An Electrocardiogram Cross-Modal Model for Echocardiogram Tasks open_in_new

2025 · original paper

DOI: 10.1007/978-3-032-04971-1_17

database Training & evaluation data

EchoCLIP paired ECG / echocardiogram-report cohort (distillation source)

train

Paired ECG and echocardiogram-report data used for cross-modal knowledge distillation from EchoCLIP; the underlying source cohort is not fully disclosed.

science Capabilities & performance