---
license: mit
tags:
  - ecg
  - medical
  - pytorch
datasets:
  - custom
language:
  - en
pipeline_tag: other
---

# ECG Over-Read: Pre-trained Models for ECG Diagnosis

Pre-trained model weights for the paper **"Beyond Machine Interpretation: Learning from Expert Over-Reads Improves ECG Diagnosis"** (Under Review of MIDL 2026).

## Models

This repository contains 4 pre-trained models:

| Model | File | Description |
|-------|------|-------------|
| **Supervised** | `supervised/best.pt` | Baseline ResNet-50 multi-label classifier |
| **CLIP** | `clip/best_cliponly.ckpt` | ECG-Text contrastive learning model |
| **NegCLIP** | `negclip/best_detail.ckpt` | CLIP with hard negative mining |
| **Self-Training** | `selftraining/best.pt` | Semi-supervised learning with FixMatch |

## Model Architecture

### ECG Encoder (All Models)
- **Architecture**: 1D ResNet-50 with bottleneck blocks (3-4-6-3)
- **Input**: 12-lead ECG signal `(batch, 12, 2500)` at 250 Hz
- **Output**: 512-dimensional embedding
- **Preprocessing**: High-pass filter (0.5 Hz), Notch filter (60 Hz), Per-lead z-score

### Text Encoder (CLIP/NegCLIP)
- **Architecture**: CLIP text encoder
- **Output**: 512-dimensional embedding (projected)

## Usage

### Download Weights

```python
from huggingface_hub import hf_hub_download

# Download supervised model
model_path = hf_hub_download(
    repo_id="tyoung089/ECG_overread",
    filename="supervised/best.pt"
)

# Load model
import torch
checkpoint = torch.load(model_path, map_location="cpu")
```

### Using with Our Code

```bash
# Clone the code repository
git clone https://github.com/YOUR_USERNAME/ecg-overread.git
cd ecg-overread

# Download all weights
pip install huggingface_hub
python download_weights.py --outdir ./weights

# Run evaluation
python Supervised/ecg_eval_cls.py \
    --csv /path/to/test.csv \
    --ckpt ./weights/supervised/best.pt \
    --label-prefix "label_diag__"
```

## Input Data Format

### ECG Signal
- **Sampling rate**: 250 Hz
- **Duration**: 10 seconds
- **Leads**: 12-lead standard ECG
- **Shape**: `(2500, 12)` or `(12, 2500)`
- **Format**: NumPy `.npy` files

### Checkpoint Format

**Supervised / Self-Training (`best.pt`)**:
```python
{
    "model": state_dict,           # Model weights
    "opt": optimizer_state,        # Optimizer state
    "sched": scheduler_state,      # Scheduler state
    "epoch": int,                  # Training epoch
    "best": float,                 # Best validation metric
    "classes": list,               # Class names
}
```

**CLIP / NegCLIP (`.ckpt`)**:
```python
{
    "model": state_dict,           # Full model (ECG + Text encoder)
    "epoch": int,
    "args": training_args,
}
```

## Training Details

| Model | Training Data | Method |
|-------|--------------|--------|
| Supervised | Labeled ECGs | Multi-label BCE loss |
| CLIP | ECG + Text pairs | Symmetric contrastive loss |
| NegCLIP | ECG + Text pairs | Contrastive loss with hard negatives |
| Self-Training | Labeled + Unlabeled | FixMatch with pseudo-labels |

## License

MIT License

## Citation

```bibtex
@inproceedings{overread2026midl,
  title={Beyond Machine Interpretation: Learning from Expert Over-Reads Improves ECG Diagnosis},
  author={Kwak et al.},
  booktitle={Under Review for Medical Imaging with Deep Learning (MIDL)},
  year={2026}
}
```

## Links

- **Code Repository**: [GitHub](https://github.com/tyoung089/ecg-overread)
- **Paper**: Coming soon