no code implementations • 23 Nov 2020 • Abel Díaz Berenguer, Hichem Sahli, Boris Joukovsky, Maryna Kvasnytsia, Ine Dirks, Mitchel Alioscha-Perez, Nikos Deligiannis, Panagiotis Gonidakis, Sebastián Amador Sánchez, Redona Brahimetaj, Evgenia Papavasileiou, Jonathan Cheung-Wai Chana, Fei Li, Shangzhen Song, Yixin Yang, Sofie Tilborghs, Siri Willems, Tom Eelbode, Jeroen Bertels, Dirk Vandermeulen, Frederik Maes, Paul Suetens, Lucas Fidon, Tom Vercauteren, David Robben, Arne Brys, Dirk Smeets, Bart Ilsen, Nico Buls, Nina Watté, Johan de Mey, Annemiek Snoeckx, Paul M. Parizel, Julien Guiot, Louis Deprez, Paul Meunier, Stefaan Gryspeerdt, Kristof De Smet, Bart Jansen, 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.
3 code implementations • 29 Jul 2020 • Sofie Tilborghs, Ine Dirks, Lucas Fidon, Siri Willems, Tom Eelbode, Jeroen Bertels, Bart Ilsen, Arne Brys, Adriana Dubbeldam, Nico Buls, Panagiotis Gonidakis, Sebastián Amador Sánchez, Annemiek Snoeckx, Paul M. Parizel, Johan de Mey, Dirk Vandermeulen, Tom Vercauteren, David Robben, Dirk Smeets, Frederik Maes, Jef Vandemeulebroucke, Paul Suetens
There is an increasing number of studies that propose to use deep learning to provide fast and accurate quantification of COVID-19 using chest CT scans.