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 " ,
}