• Corpus ID: 227151836

Explainable-by-design Semi-Supervised Representation Learning for COVID-19 Diagnosis from CT Imaging

@article{Berenguer2020ExplainablebydesignSR,
  title={Explainable-by-design Semi-Supervised Representation Learning for COVID-19 Diagnosis from CT Imaging},
  author={Abel D'iaz Berenguer and Hichem Sahli and B. Joukovsky and Maryna Kvasnytsia and Ine Dirks and Mitchel Alioscha-P{\'e}rez and Nikolaos Deligiannis and Panagiotis Gonidakis and Sebasti'an Amador S'anchez and Redona Brahimetaj and Evgenia Papavasileiou and Jonathan Cheung-Wai Chana and Fei Li and Shangzhen Song and Yixin Yang and Sofie Tilborghs and Siri Willems and Tom Eelbode and J. Bertels and Dirk Vandermeulen and Frederik Maes and Paul Suetens and Lucas Fidon and Tom Vercauteren and David Robben and Arne Brys and Dirk Smeets and Bart Ilsen and Nico Buls and Nina Watt'e and Johan de Mey and Annemie Snoeckx and Paul M. Parizel and Julien Guiot and Louis Deprez and Paul Meunier and Stefaan Gryspeerdt and Kristof de Smet and Bart Jansen and Jef Vandemeulebroucke},
  journal={ArXiv},
  year={2020},
  volume={abs/2011.11719}
}
Our motivating application is a real-world problem: COVID-19 classification from CT imaging, for which we present an explainable Deep Learning approach based on a semi-supervised classification pipeline that employs variational autoencoders to extract efficient feature embedding. We have optimized the architecture of two different networks for CT images: (i) a novel conditional variational autoencoder (CVAE) with a specific architecture that integrates the class labels inside the encoder layers… 

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