EchoNet-Synthetic: Privacy-preserving Video Generation for Safe Medical Data Sharing
<|reference_start|>EchoNet-Synthetic: Privacy-preserving Video Generation for Safe Medical Data Sharing: To make medical datasets accessible without sharing sensitive patient information, we introduce a novel end-to-end approach for generative de-identification of dynamic medical imaging data. Until now, generative methods have faced constraints in terms of fidelity, spatio-temporal coherence, and the length of generation, failing to capture the complete details of dataset distributions. We present a model designed to produce high-fidelity, long and complete data samples with near-real-time efficiency and explore our approach on a challenging task: generating echocardiogram videos. We develop our generation method based on diffusion models and introduce a protocol for medical video dataset anonymization. As an exemplar, we present EchoNet-Synthetic, a fully synthetic, privacy-compliant echocardiogram dataset with paired ejection fraction labels. As part of our de-identification protocol, we evaluate the quality of the generated dataset and propose to use clinical downstream tasks as a measurement on top of widely used but potentially biased image quality metrics. Experimental outcomes demonstrate that EchoNet-Synthetic achieves comparable dataset fidelity to the actual dataset, effectively supporting the ejection fraction regression task. Code, weights and dataset are available at https://github.com/HReynaud/EchoNet-Synthetic.<|reference_end|>
@article{reynaud2024echonet-synthetic:,
    title={EchoNet-Synthetic: Privacy-preserving Video Generation for Safe Medical
  Data Sharing},
    author={Hadrien Reynaud, Qingjie Meng, Mischa Dombrowski, Arijit Ghosh, Thomas
  Day, Alberto Gomez, Paul Leeson, Bernhard Kainz},
    journal={arXiv preprint arXiv:2406.00808},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.00808},
    primaryClass={cs.CV}
}