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
Allahabadi, Himanshi, Amann, Julia, Balot, Isabelle, Beretta, Andrea, Binkley, Charles, Bozenhard, Jonas, Bruneault, Frederick, Brusseau, James, Candemir, Sema, Cappellini, Luca Alessandro, 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-Maria, 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.
Published in IEEE transactions on technology and society (01.12.2022)
Published in IEEE transactions on technology and society (01.12.2022)
Get full text
Journal Article