Publication
Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients
Himanshi Allahabadi, Julia Amann, Isabelle Balot, Andrea Beretta, Charles Binkley, Jonas Bozenhard, Frederick Bruneault, James Brusseau, Sema Candemir, Luca Alessandro Cappellini, Subrata Chakraborty, Nicoleta Cherciu, Christina Cociancig, Megan Coffee, Irene Ek, Leonardo Espinosa-Leal, Davide Farina, Genevieve Fieux-Castagnet, Thomas Frauenfelder, Alessio Gallucci, Guya Giuliani, Adam Golda, Irmhild van Halem, Elisabeth Hildt, Sune Holm, Georgios Kararigas, Sebastien A. Krier, Ulrich Kuhne, Francesca Lizzi, Vince I. Madai, Aniek F. Markus, Serg Masis, Emilie Wiinblad Mathez, Francesco Mureddu, Emanuele Neri, Walter Osika, Matiss Ozols, Cecilia Panigutti, Brendan Parent, Francesca Pratesi, Pedro A. Moreno-Sanchez, Giovanni Sartor, Mattia Savardi, Alberto Signoroni, Hanna-Maria Sormunen, Andy Spezzatti, Adarsh Srivastava, Annette F. Stephansen, Lau Bee Theng, Jesmin Jahan Tithi, Jarno Tuominen, Steven Umbrello, Filippo Vaccher, Dennis Vetter, Magnus Westerlund, Renee Wurth, Roberto V. Zicari
IEEE Transactions on Technology and Society, December 2022, Institute of Electrical & Electronics Engineers (IEEE)
DOI: 10.1109/tts.2022.3195114