Explainable-by-design Semi-Supervised Representation Learning for COVID-19 Diagnosis from CT Imaging
| Autor: | Abel Díaz Berenguer, Hichem Sahli, Boris Joukovsky, Maryna Kvasnytsia, Ine Dirks, Mitchel Alioscha-Perez, Nikos Deligiannis, Panagiotis Gonidakis, Sebastian Amador, 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, Parizel, Paul M., Julien Guiot, Louis Deprez, Paul Meunier, Stefaan Gryspeerdt, Kristof De Smet, Bart Jansen, Jef Vandemeulebroucke |
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| Přispěvatelé: | Faculty of Engineering, Electronics and Informatics, Audio Visual Signal Processing, Artificial Intelligence supported Modelling in clinical Sciences, Clinical sciences, Medicine and Pharmacy academic/administration, Medical Imaging, Radiology, Supporting clinical sciences, Faculty of Medicine and Pharmacy, Body Composition and Morphology, Translational Imaging Research Alliance |
| Předmět: |
semi-supervised learning
FOS: Computer and information sciences diagnosis Computer Vision and Pattern Recognition (cs.CV) Image and Video Processing (eess.IV) Computer Science - Computer Vision and Pattern Recognition COVID-19 imaging Electrical Engineering and Systems Science - Image and Video Processing ComputingMethodologies_PATTERNRECOGNITION FOS: Electrical engineering electronic engineering information engineering eess.IV cs.CV CT |
| Zdroj: | Vrije Universiteit Brussel University of Western Australia |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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