• Medientyp: E-Artikel
  • Titel: 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
  • Beteiligte: 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; [...]
  • Erschienen: Institute of Electrical and Electronics Engineers (IEEE), 2022
  • Erschienen in: IEEE Transactions on Technology and Society
  • Sprache: Nicht zu entscheiden
  • DOI: 10.1109/tts.2022.3195114
  • ISSN: 2637-6415
  • Schlagwörter: Automotive Engineering
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