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)
Institute of Electrical and Electronics Engineers (IEEE)
2022