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@@ -16,93 +16,16 @@ GEM is a multimodal LLM unifying ECG time series, 12-lead ECG images and text fo
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  ## πŸ”₯ Updates
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  #### Paper: πŸ“„ [Arxiv](https://arxiv.org/pdf/2503.06073)
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  #### Model: πŸ€— [GEM](https://huggingface.co/LANSG/GEM)
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  #### Data: πŸ€— [ECG-Grounding](https://huggingface.co/datasets/LANSG/ECG-Grounding)
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- ## Setup
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-
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- ```shell
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- git clone https://github.com/lanxiang1017/GEM.git
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- bash GEM/setup.sh
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- ```
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-
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- ## Data Preparation
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-
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- Please download required data:
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-
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- ECG:
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- - [MIMIC-IV](https://physionet.org/content/mimic-iv-ecg/1.0/)
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- - [PTB-XL](https://physionet.org/content/ptb-xl/1.0.3/)
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- - [Code-15%](https://zenodo.org/records/4916206)
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- - [CPSC 2018](https://physionet.org/content/challenge-2020/1.0.2/training/cpsc_2018/)
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- - [CSN](https://physionet.org/content/ecg-arrhythmia/1.0.0/)
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- - [G12E](https://physionet.org/content/challenge-2020/1.0.2/training/georgia/)
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-
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- Images:
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- - [ECG-Grounding-Images](https://huggingface.co/datasets/LANSG/ECG-Grounding) (mimic_gen)
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- - [ECG-Bench](https://huggingface.co/datasets/PULSE-ECG/ECGBench)
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-
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- After downloading all of them, organize the data as follows in `./data`,
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-
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- ```
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- β”œβ”€β”€ ecg_timeseries
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- └── champan-shaoxing
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- └── code15
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- └── cpsc2018
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- └── ptbxl
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- └── georgia
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- └── mimic-iv
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- β”œβ”€β”€ ecg_images
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- └── cod15_v4
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- └── csn_aug_all_layout_papersize
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- └── csn_ori_layout_papersize
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- └── csn_part_noise_layout_papersize
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- └── gen_images
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- └── mimic_gen
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- └── mimic
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- └── mimic_v4
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- └── ptb-xl
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- β”œβ”€β”€ ecg_bench
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- └── images
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- └── jsons
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- β”œβ”€β”€ ecg_jsons
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- └── ECG_Grounding_30k.json
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- └── ECG_Grounding_130k.json
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- β”œβ”€β”€ ecg_grounding_test_data
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- └── ecg-grounding-test.json
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-
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- ```
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-
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- ## Pretrained Model Preparation
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-
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- Pretrained ECG Encoder:
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- - [ECG-CoCa](https://github.com/YubaoZhao/ECG-Chat) : place it in ```GEM/ecg_coca/open_clip/checkpoint```
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-
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- Pretrained MLLMs:
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- - [PULSE](https://huggingface.co/PULSE-ECG/PULSE-7B)
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- - [LLaVA](https://huggingface.co/liuhaotian/llava-v1.6-vicuna-7b)
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-
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- ## Train
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-
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- ```bash GEM/scripts/train_gem.sh```
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-
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- ## Evaluation
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-
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- For ECG-Grounding:
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- - step 1. generate interpretations: ```GEM/evaluation/gem_bench/bench_ecggrounding.sh```
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- - step 2. process interpretations: ```GEM/gem_evaluation/process_gem_outputs.ipynb```
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- - step 3. generate GPT evaluation reports: ```GEM/gem_evaluation/generate_gpt_eval.py```
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- - step 4. process evaluation reports and get scores: ```GEM/gem_evaluation/process_grounding_scores.ipynb```
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-
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- For ECG-Bench:
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- - step 1. generate results: ```GEM/evaluation/gem_bench/bench_ecggrounding.sh```
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- - step 2. evaluate results: ```GEM/evaluation/evaluate_ecgbench.py```
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- - step 3. evaluate reports: ```GEM/evaluation/eval_report.py```
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-
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- *Note: You'll need to specify result paths first in all evaluation scripts*
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  ## Citation
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@@ -119,10 +42,3 @@ If you find GEM helpful for your research and applications, please cite our pape
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  url={https://arxiv.org/abs/2503.06073},
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  }
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  ```
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-
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- ## Acknowledgement
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- We thank the authors of [PULSE](https://github.com/AIMedLab/PULSE/tree/dev) and [ECG-Chat](https://github.com/YubaoZhao/ECG-Chat) for their publicly released models, datasets, and training codes.
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-
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- ## Code
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-
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- Code: https://github.com/lanxiang1017/GEM
 
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  ## πŸ”₯ Updates
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+ #### Project Page: πŸ“– [Page](https://www.lanxplanet.com/GEM-ECG/)
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+
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  #### Paper: πŸ“„ [Arxiv](https://arxiv.org/pdf/2503.06073)
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+ #### Code: πŸ’» [GitHub](https://github.com/lanxiang1017/GEM)
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+
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  #### Model: πŸ€— [GEM](https://huggingface.co/LANSG/GEM)
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  #### Data: πŸ€— [ECG-Grounding](https://huggingface.co/datasets/LANSG/ECG-Grounding)
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  ## Citation
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  url={https://arxiv.org/abs/2503.06073},
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  }
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  ```