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--- |
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license: mit |
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tags: |
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- ecg |
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- medical |
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- pytorch |
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datasets: |
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- custom |
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language: |
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- en |
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pipeline_tag: other |
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--- |
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# ECG Over-Read: Pre-trained Models for ECG Diagnosis |
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Pre-trained model weights for the paper **"Beyond Machine Interpretation: Learning from Expert Over-Reads Improves ECG Diagnosis"** (Under Review of MIDL 2026). |
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## Models |
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This repository contains 4 pre-trained models: |
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| Model | File | Description | |
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|-------|------|-------------| |
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| **Supervised** | `supervised/best.pt` | Baseline ResNet-50 multi-label classifier | |
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| **CLIP** | `clip/best_cliponly.ckpt` | ECG-Text contrastive learning model | |
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| **NegCLIP** | `negclip/best_detail.ckpt` | CLIP with hard negative mining | |
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| **Self-Training** | `selftraining/best.pt` | Semi-supervised learning with FixMatch | |
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## Model Architecture |
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### ECG Encoder (All Models) |
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- **Architecture**: 1D ResNet-50 with bottleneck blocks (3-4-6-3) |
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- **Input**: 12-lead ECG signal `(batch, 12, 2500)` at 250 Hz |
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- **Output**: 512-dimensional embedding |
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- **Preprocessing**: High-pass filter (0.5 Hz), Notch filter (60 Hz), Per-lead z-score |
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### Text Encoder (CLIP/NegCLIP) |
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- **Architecture**: CLIP text encoder |
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- **Output**: 512-dimensional embedding (projected) |
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## Usage |
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### Download Weights |
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```python |
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from huggingface_hub import hf_hub_download |
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# Download supervised model |
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model_path = hf_hub_download( |
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repo_id="tyoung089/ECG_overread", |
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filename="supervised/best.pt" |
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) |
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# Load model |
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import torch |
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checkpoint = torch.load(model_path, map_location="cpu") |
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``` |
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### Using with Our Code |
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```bash |
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# Clone the code repository |
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git clone https://github.com/YOUR_USERNAME/ecg-overread.git |
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cd ecg-overread |
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# Download all weights |
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pip install huggingface_hub |
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python download_weights.py --outdir ./weights |
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# Run evaluation |
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python Supervised/ecg_eval_cls.py \ |
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--csv /path/to/test.csv \ |
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--ckpt ./weights/supervised/best.pt \ |
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--label-prefix "label_diag__" |
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``` |
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## Input Data Format |
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### ECG Signal |
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- **Sampling rate**: 250 Hz |
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- **Duration**: 10 seconds |
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- **Leads**: 12-lead standard ECG |
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- **Shape**: `(2500, 12)` or `(12, 2500)` |
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- **Format**: NumPy `.npy` files |
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### Checkpoint Format |
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**Supervised / Self-Training (`best.pt`)**: |
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```python |
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{ |
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"model": state_dict, # Model weights |
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"opt": optimizer_state, # Optimizer state |
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"sched": scheduler_state, # Scheduler state |
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"epoch": int, # Training epoch |
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"best": float, # Best validation metric |
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"classes": list, # Class names |
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} |
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``` |
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**CLIP / NegCLIP (`.ckpt`)**: |
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```python |
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{ |
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"model": state_dict, # Full model (ECG + Text encoder) |
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"epoch": int, |
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"args": training_args, |
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} |
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``` |
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## Training Details |
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| Model | Training Data | Method | |
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|-------|--------------|--------| |
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| Supervised | Labeled ECGs | Multi-label BCE loss | |
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| CLIP | ECG + Text pairs | Symmetric contrastive loss | |
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| NegCLIP | ECG + Text pairs | Contrastive loss with hard negatives | |
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| Self-Training | Labeled + Unlabeled | FixMatch with pseudo-labels | |
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## License |
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MIT License |
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## Citation |
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```bibtex |
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@inproceedings{overread2026midl, |
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title={Beyond Machine Interpretation: Learning from Expert Over-Reads Improves ECG Diagnosis}, |
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author={Kwak et al.}, |
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booktitle={Under Review for Medical Imaging with Deep Learning (MIDL)}, |
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year={2026} |
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} |
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``` |
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## Links |
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- **Code Repository**: [GitHub](https://github.com/tyoung089/ecg-overread) |
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- **Paper**: Coming soon |
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