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Jeff4899/
202509_A4C_EF

Name: 202509_A4C_EF
Availability: InStock

cc-by-nc-4.0

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A4C EF Prediction Model

Citation

If you use this dataset, please cite the following works:

Primary manuscript
Gao et al., Learning from Scarce Labels: Multi-View Echocardiography for Ejection Fraction Prediction.
Preprint, 2025.

Prior conference paper
Gao, Z., Yurk, D., & Abu-Mostafa, Y. S. (2025). Machine Learning with Scarce Data: Ejection Fraction Prediction Using PLAX View.
In Medical Imaging with Deep Learning (MIDL).
https://openreview.net/forum?id=JEN5FzeFZj

BibTeX

bibtex

@inproceedings{gao2025machine,
  title     = {Machine Learning with Scarce Data: Ejection Fraction Prediction Using {PLAX} View},
  author    = {Gao, Zhiyuan and Yurk, Dominic and Abu-Mostafa, Yaser S.},
  booktitle = {Medical Imaging with Deep Learning},
  year      = {2025},
  url       = {https://openreview.net/forum?id=JEN5FzeFZj}
}

---

For more details, see the companion GitHub repo:
👉 Jeffrey4899/PLAX_EF_Labels_202509

Model Performance

Mean Absolute Error (MAE): ~7%
See publication for full results
Dataset: Labels derived from the EchoNet-Dynamic dataset.

Intended Use

Research and education only
Demonstrates feasibility of EF prediction from the apical four-chamber (A4C) view
Input: MP4 (H.264), ~64 frames, resized 112×112
Output: Scalar EF estimate (0–100%)

Limitations

Trained only on A4C view; does not generalize to other views (e.g., PLAX, PSAX).
Not for clinical use.
Assumes reasonable video quality and clip length.

Disclaimer

⚠️ This model is not a medical device and must not be used for clinical diagnosis or treatment.

How to Use

python

from huggingface_hub import hf_hub_download
import torch, torchvision

ckpt = hf_hub_download("Jeff4899/A4C_EF", "a4c_model.pth")
model = torchvision.models.video.r2plus1d_18(weights=None)
model.fc = torch.nn.Linear(model.fc.in_features, 1)
state = torch.load(ckpt, map_location="cpu")
model.load_state_dict(state, strict=False)
model.eval()

Jeff4899/202509_A4C_EF