DERI: Cross-Modal ECG Representation Learning with Deep ECG-Report Interaction
Authors: Jian Chen, Xiaoru Dong, Wei Wang, Shaorui Zhou, Lequan Yu, Xiping Hu
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments with various settings are conducted on various datasets to show the superior performance of our DERI. |
| Researcher Affiliation | Academia | 1Artificial Intelligence Research Institute, Shenzhen MSU-BIT University 2School of Intelligent System Engineering, Sun Yat-sen University 3School of Computing and Data Science, The University of Hong Kong 4School of Medical Technology, Beijing Institute of Technology EMAIL, xrdong,EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes its methodology in prose and mathematical equations in Section 3 (Methodology) but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is released on https://github.com/cccccj-03/DERI. |
| Open Datasets | Yes | MIMIC-ECG. The pre-training process of our proposed DERI model is conducted on the MIMIC-ECG dataset [Gow et al., 2023]... PTBXL [Wagner et al., 2020] contains 21,837 12-lead ECG signals... CPSC2018 [Liu et al., 2018] contains 6,877 12-lead ECG recordings... Chapman-Shaoxing-Ningbo (CSN) [Zheng et al., 2022] contains 45,152 12-lead ECG recordings... |
| Dataset Splits | Yes | For linear probing, we add a new linear classifier and freeze all other parameters in our DERI. We adopt three different settings, which utilize 1%, 10%, and 100% of the training data. |
| Hardware Specification | Yes | We conduct all the pre-trained experiments on 4 NVIDIA GeForce RTX 4090 GPUs with a batch size of 512. |
| Software Dependencies | No | For the encoders used for ECG signals and reports, we adopt a randomly initialized 1D-ResNet18 and the Med-CPT [Jin et al., 2023], respectively... After we obtain the final representation of ECG, we adopt GPT-2 as the text decoder to construct an encoder-decoder structure since Distil GPT2 [Sanh, 2019] has shown its great performance on report generation [Wang et al., 2024]. |
| Experiment Setup | Yes | We use the AdamW optimizer with a learning rate of 1e-3 and a weight decay of 1e-8. The epoch for pre-training is set as 50 with a cosine annealing scheduler to adjust the learning rate. We conduct all the pre-trained experiments on 4 NVIDIA GeForce RTX 4090 GPUs with a batch size of 512. |