File size: 3,554 Bytes
6fad9b5 3b497b4 6fad9b5 62e4479 6fad9b5 62e4479 6fad9b5 62e4479 6fad9b5 3b497b4 6fad9b5 3b497b4 6fad9b5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 |
---
license: apache-2.0
datasets:
- PULSE-ECG/ECGInstruct
- PULSE-ECG/ECGBench
language:
- en
pipeline_tag: image-text-to-text
tags:
- medical
---
# PULSE-7B
Dataset for paper "Teach Multimodal LLMs to Comprehend Electrocardiographic Images".
🌐 Project Page: [https://aimedlab.github.io/PULSE/](https://aimedlab.github.io/PULSE/)
📄 Paper: [https://arxiv.org/abs/2410.19008](https://arxiv.org/abs/2410.19008)
🧑💻 Code: [https://github.com/AIMedLab/PULSE](https://github.com/AIMedLab/PULSE)
👩⚕️ ECGInstruct(Training): [https://huggingface.co/datasets/PULSE-ECG/ECGInstruct](https://huggingface.co/datasets/PULSE-ECG/ECGInstruct)
⚖️ ECGBench(Testing): [https://huggingface.co/datasets/PULSE-ECG/ECGBench](https://huggingface.co/datasets/PULSE-ECG/ECGBench)
## Introduction
We introduce **PULSE-7B**, a multimodal large language model (MLLM) specifically designed for ECG image interpretation. Leveraging the comprehensive **ECGInstruct** dataset, which contains over one million instruction-tuning samples, PULSE-7B is tailored to handle a wide range of ECG-related tasks drawn from diverse data sources. While traditional ECG interpretation methods are often constrained by their reliance on raw physiological signals and limited to specific cardiac conditions, PULSE-7B addresses these limitations by enabling robust interpretation of both printed and digital ECG images, making it especially valuable in resource-limited settings where access to raw signals may be restricted. In conjunction with the introduction of **ECGBench**, a benchmark that includes four key tasks spanning nine datasets, our experiments demonstrate that PULSE-7B establishes new state-of-the-art performance, surpassing general MLLMs with an average accuracy improvement of 15% to 30%. This model showcases the potential to significantly advance ECG image interpretation, providing a more versatile and accurate tool for clinical practice.
Overall performance of PULSE-7B on ECGBench
## Model Performance
### In-domain
### Out-of-domain
## Case Study
<img src="https://cdn-uploads.huggingface.co/production/uploads/640701cb4dc5f2846c91d4eb/4opsQpGP_SiSnfQbZj22b.png" alt="ECG Image" width="700"/>
<!--  -->
<img src="https://cdn-uploads.huggingface.co/production/uploads/640701cb4dc5f2846c91d4eb/qelm-5ki0g_OEJoSPS8p_.png" alt="ECG Image" width="700"/>
<!--  -->
<img src="https://cdn-uploads.huggingface.co/production/uploads/640701cb4dc5f2846c91d4eb/YfKUgi3lsXRu4epinS9BY.png" alt="ECG Image" width="700"/>
<!--  -->
## 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}
}
```
|