  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.
