论文信息 - 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 - 字舞流文

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

Lau Bee Theng | Pedro A. Moreno-Sanchez | Jesmin Jahan Tithi | G. Sartor | E. Neri | R. Zicari | E. Hildt | T. Frauenfelder | S. Candemir | Cecilia Panigutti | J. Amann | M. Coffee | Leonardo Espinosa-Leal | Jarno Tuominen | A. Stephansen | D. Farina | S. Masis | W. Osika | Sune Holm | F. Mureddu | A. Markus | Mattia Savardi | A. Signoroni | Charles E Binkley | V. Madai | Andrea Beretta | A. Golda | G. Kararigas | J. Brusseau | U. Kühne | Francesca Lizzi | M. Ozols | Magnus Westerlund | Frédérick Bruneault | Alessio Gallucci | Andy Spezzatti | Steven Umbrello | Dennis Vetter | Emilie Wiinblad Mathez | F. Vaccher | Brendan Parent | Renee Wurth | Francesca Pratesi | Himanshi Allahabadi | Isabelle Balot | Jonas Bozenhard | L. Cappellini | Subrata Chakraborty | Nicoleta Cherciu | Christina Cociancig | Irene Ek | Genevieve Fieux-Castagnet | Guya Giuliani | Irmhild van Halem | Sebastien A. Krier | Hanna-Maria Sormunen | Adarsh Srivastava