license: apache-2.0
configs:
- config_name: ECGInstruct
data_files:
- split: train
path: ECGInstruct.json
ECGInstruct
Dataset for paper "Teach Multimodal LLMs to Comprehend Electrocardiographic Images".
🌐 Project Page: https://aimedlab.github.io/PULSE/
📄 Paper: https://arxiv.org/abs/2410.19008
🧑💻 Code: https://github.com/AIMedLab/PULSE
🤗 Model: https://huggingface.co/PULSE-ECG/PULSE-7B
⚖️ ECGBench: https://huggingface.co/datasets/PULSE-ECG/ECGBench
Introduction
ECGInstruct is a comprehensive and large-scale instruction-tuning dataset designed for ECG image interpretation. (1) The ECG images in this dataset are generated from raw signal recordings and include a range of distortions that simulate real-world printed ECG images. (2) ECGInstruct is carefully curated, drawing from clinician-defined ECG tasks, original diagnoses, clinical reports, and a variety of task types. To ensure high quality, additional checks are applied to filter out lower-scored instructions.
Dataset Statistics
Dataset Examples
Citation
If you find this work helpful, please cite our paper:
@article{liu2024teach,
title={Teach Multimodal LLMs to Comprehend Electrocardiographic Images},
author={Ruoqi Liu, Yuelin Bai, Xiang Yue, Ping Zhang},
journal={arXiv preprint arXiv:2410.19008},
year={2024}
}