Assessing Trustworthy AI in times of COVID-19. Deep Learning for predicting a multi-regional score conveying the degree of lung compromise in COVID-19 patients

Allahabadi, Himanshi;Amann, Julia;Balot, Isabelle;Beretta, Andrea;Binkley, Charles;Bozenhard, Jonas;Bruneault, Frederick;Brusseau, James;Candemir, Sema;Cappellini, Luca Alessandro;Castagnet, Genevieve Fieux;Chakraborty, Subrata;Cherciu, Nicoleta;Cociancig, Christina;Coffee, Megan;Ek, Irene;Espinosa-Leal, Leonardo;Farina, Davide;Fieux-Castagnet, Genevieve;Frauenfelder, Thomas;Gallucci, Alessio;Giuliani, Guya;Golda, Adam;van Halem, Irmhild;Hildt, Elisabeth;Holm, Sune;Kararigas, Georgios;Krier, Sebastien A.;Kuhne, Ulrich;Lizzi, Francesca;Madai, Vince I.;Markus, Aniek F.;Masis, Serg;Mathez, Emilie Wiinblad;Mureddu, Francesco;Neri, Emanuele;Osika, Walter;Ozols, Matiss;Panigutti, Cecilia;Parent, Brendan;Pratesi, Francesca;Moreno-Sanchez, Pedro A.;Sartor, Giovanni;Savardi, Mattia;Signoroni, Alberto;Sormunen, Hanna;Spezzatti, Andy;Srivastava, Adarsh;Stephansen, Annette F.;Theng, Lau Bee;Tithi, Jesmin Jahan;Tuominen, Jarno;Umbrello, Steven;Vaccher, Filippo;Vetter, Dennis;Westerlund, Magnus;Wurth, Renee;Zicari, Roberto V.

2022-01-01