# Contrastive Cross-Modal Learning for Infusing Chest X-ray Knowledge into ECGs

arXiv: https://arxiv.org/abs/2506.19329 · HTML: https://mlsanity.com/paper/2506.19329/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 31 references that are in the map.
- First posted on arXiv 24 Jun 2025.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients](https://mlsanity.com/paper/2005.13249/) (2020)
- [TorchXRayVision: A library of chest X-ray datasets and models](https://mlsanity.com/paper/2111.00595/) (2021)
- [Subject-Aware Contrastive Learning for Biosignals](https://mlsanity.com/paper/2007.04871/) (2020)
- [Best of Both Worlds: Multimodal Contrastive Learning with Tabular and Imaging Data](https://mlsanity.com/paper/2303.14080/) (2023)
- [Zero-Shot ECG Classification with Multimodal Learning and Test-time Clinical Knowledge Enhancement](https://mlsanity.com/paper/2403.06659/) (2024)
- [MedFuse: Multi-modal fusion with clinical time-series data and chest X-ray images](https://mlsanity.com/paper/2207.07027/) (2022)
- [DrFuse: Learning Disentangled Representation for Clinical Multi-Modal Fusion with Missing Modality and Modal Inconsistency](https://mlsanity.com/paper/2403.06197/) (2024)
- [3KG: Contrastive Learning of 12-Lead Electrocardiograms using Physiologically-Inspired Augmentations](https://mlsanity.com/paper/2106.04452/) (2021)
- [Multimodal Pretraining of Medical Time Series and Notes](https://mlsanity.com/paper/2312.06855/) (2023)
- [Data Augmentation for Electrocardiograms](https://mlsanity.com/paper/2204.04360/) (2022)
- [MoRE: Multi-Modal Contrastive Pre-training with Transformers on X-Rays, ECGs, and Diagnostic Report](https://mlsanity.com/paper/2410.16239/) (2024)

## Abstract (arXiv)

Modern diagnostic workflows are increasingly multimodal, integrating diverse data sources such as medical images, structured records, and physiological time series. Among these, electrocardiograms (ECGs) and chest X-rays (CXRs) are two of the most widely used modalities for cardiac assessment. While CXRs provide rich diagnostic information, ECGs are more accessible and can support scalable early warning systems. In this work, we propose CroMoTEX, a novel contrastive learning-based framework that leverages chest X-rays during training to learn clinically informative ECG representations for multiple cardiac-related pathologies: cardiomegaly, pleural effusion, and edema. Our method aligns ECG and CXR representations using a novel supervised cross-modal contrastive objective with adaptive hard negative weighting, enabling robust and task-relevant feature learning. At test time, CroMoTEX relies solely on ECG input, allowing scalable deployment in real-world settings where CXRs may be unavailable. Evaluated on the large-scale MIMIC-IV-ECG and MIMIC-CXR datasets, CroMoTEX outperforms baselines across all three pathologies, achieving up to 78.31 AUROC on edema. Our code is available at github.com/vineetpmoorty/cromotex.