Abel Díaz Berenguer,
Abel Díaz Berenguer,
Hichem Sahli,
Hichem Sahli, Boris Joukovsky, Boris Joukovsky,
Maryna Kvasnytsia,
Maryna Kvasnytsia,
Ine Dirks,
Ine Dirks, Mitchel Perez Gonzalez, Mitchel Perez Gonzalez,
Nikos Deligiannis,
Nikos Deligiannis,
Panagiotis Gonidakis,
Panagiotis Gonidakis, Sebastián Amador Sánchez, Sebastián Amador Sánchez,
Redona Brahimetaj,
Redona Brahimetaj,
Evgenia Papavasileiou,
Evgenia Papavasileiou,
Jonathan C-W Chan,
Jonathan C-W Chan, Fei Li, Fei Li, Shangzhen Song, Shangzhen Song, Yixin Yang, Yixin Yang, Sofie Tilborghs, Sofie Tilborghs, Siri Willems, Siri Willems, Tom Eelbode, Tom Eelbode, Jeroen Bertels, Jeroen Bertels, Dirk Vandermeulen, Dirk Vandermeulen, Frederik Maes, Frederik Maes, Paul Suetens, Paul Suetens, Lucas Fidon, Lucas Fidon, Tom Vercauteren, Tom Vercauteren, David Robben, David Robben, Arne Brys, Arne Brys, Dirk Smeets, Dirk Smeets, Bart Ilsen, Bart Ilsen, , , Nina Watté, Nina Watté, , , Annemiek Snoeckx, Annemiek Snoeckx, Paul M. Parizel, Paul M. Parizel, Julien Guiot, Julien Guiot, Louis Deprez, Louis Deprez, Paul Meunier, Paul Meunier, Stefaan Gryspeerdt, Stefaan Gryspeerdt, Kristof De Smet, Kristof De Smet,
Bart Jansen,
Bart Jansen,
Jef Vandemeulebroucke,
Jef Vandemeulebroucke
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 and uses side information with shared attention layers for the encoder, which make the most of the contextual clues for representation learning, and (ii) a downstream convolutional neural network for supervised classification using the encoder structure of the CVAE. With the explainable classification results, the proposed diagnosis system is very effective for COVID-19 classification. Based on the promising results obtained qualitatively and quantitatively, we envisage a wide deployment of our developed technique in large-scale clinical studies.Code is available at https://git.etrovub.be/AVSP/ct-based-covid-19-diagnostic-tool.git.
Berenguer, AD, Sahli, H, Joukovsky, B, Kvasnytsia, M, Dirks, I, Alioscha-Perez, M, Deligiannis, N, Gonidakis, P, Sánchez, SA, Brahimetaj, R, Papavasileiou, E, Chana, JC-W, Li, F, Song, S, Yang, Y, Tilborghs, S, Willems, S, Eelbode, T, Bertels, J, Vandermeulen, D, Maes, F, Suetens, P, Fidon, L, Vercauteren, T, Robben, D, Brys, A, Smeets, D, Ilsen, B, Buls, N, Watté, N, Mey, JD, Snoeckx, A, Parizel, PM, Guiot, J, Deprez, L, Meunier, P, Gryspeerdt, S, Smet, KD, Jansen, B & Vandemeulebroucke, J 2020, 'Explainable-by-design Semi-Supervised Representation Learning for COVID-19 Diagnosis from CT Imaging', ArXiv.org, vol. 2020.
Berenguer, A. D., Sahli, H., Joukovsky, B., Kvasnytsia, M., Dirks, I., Alioscha-Perez, M., Deligiannis, N., Gonidakis, P., Sánchez, S. A., Brahimetaj, R., Papavasileiou, E., Chana, J. C.-W., Li, F., Song, S., Yang, Y., Tilborghs, S., Willems, S., Eelbode, T., Bertels, J., ... Vandemeulebroucke, J. (2020). Explainable-by-design Semi-Supervised Representation Learning for COVID-19 Diagnosis from CT Imaging. ArXiv.org, 2020.
@article{c627301790384fac83de80c1027c54f7,
title = "Explainable-by-design Semi-Supervised Representation Learning for COVID-19 Diagnosis from CT Imaging",
abstract = " 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 and uses side information with shared attention layers for the encoder, which make the most of the contextual clues for representation learning, and (ii) a downstream convolutional neural network for supervised classification using the encoder structure of the CVAE. With the explainable classification results, the proposed diagnosis system is very effective for COVID-19 classification. Based on the promising results obtained qualitatively and quantitatively, we envisage a wide deployment of our developed technique in large-scale clinical studies.Code is available at https://git.etrovub.be/AVSP/ct-based-covid-19-diagnostic-tool.git. ",
keywords = "eess.IV, cs.CV",
author = "Berenguer, \{Abel D{\'i}az\} and Hichem Sahli and Boris Joukovsky and Maryna Kvasnytsia and Ine Dirks and Mitchel Alioscha-Perez and Nikos Deligiannis and Panagiotis Gonidakis and S{\'a}nchez, \{Sebasti{\'a}n Amador\} and Redona Brahimetaj and Evgenia Papavasileiou and Chana, \{Jonathan Cheung-Wai\} and Fei Li and Shangzhen Song and Yixin Yang and Sofie Tilborghs and Siri Willems and Tom Eelbode and Jeroen 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 Mey, \{Johan de\} and Annemiek Snoeckx and Parizel, \{Paul M.\} and Julien Guiot and Louis Deprez and Paul Meunier and Stefaan Gryspeerdt and Smet, \{Kristof De\} and Bart Jansen and Jef Vandemeulebroucke",
year = "2020",
month = dec,
day = "2",
language = "English",
volume = "2020",
journal = "ArXiv.org",
issn = "2331-8422",
}