bibtext: @InProceedings{ Rey_EchoNetSynthetic_MICCAI2024,
                   author = { Reynaud, Hadrien and Meng, Qingjie and Dombrowski, Mischa and Ghosh, Arijit and Day, Thomas and Gomez, Alberto and Leeson, Paul and Kainz, Bernhard },
                   title = { { EchoNet-Synthetic: Privacy-preserving Video Generation for Safe Medical Data Sharing } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15007 },
                   month = {October},
                   pages = { pending },
                }
abstract: 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.
title: EchoNet-Synthetic: Privacy-preserving Video Generation for Safe Medical Data Sharing
authors: Reynaud, Hadrien Meng, Qingjie Dombrowski, Mischa Ghosh, Arijit Day, Thomas Gomez, Alberto Leeson, Paul Kainz, Bernhard