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
在新冠疫情期间评估可信赖的人工智能：利用深度学习预测反映新冠肺炎患者肺部受损程度的多区域评分
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doi:10.1109/TTS.2022.3195114
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
