Rapid quantification of COVID-19 pneumonia burden from computed tomography with convolutional long short-term memory networks-Reference-Cited by-同舟云学术

Rapid quantification of COVID-19 pneumonia burden from computed tomography with convolutional long short-term memory networks

Published:2022-09-06 Issue:05 Volume:9 Page:
ISSN:2329-4302
Container-title:Journal of Medical Imaging
language:
Short-container-title:J. Med. Imag.

Author:

Killekar Aditya¹,Grodecki Kajetan²,Lin Andrew¹,Cadet Sebastien¹,McElhinney Priscilla¹,Razipour Aryabod¹,Chan Cato¹,Pressman Barry D.¹,Julien Peter¹,Chen Peter¹,Simon Judit³,Maurovich-Horvat Pal³,Gaibazzi Nicola⁴,Thakur Udit⁵,Mancini Elisabetta⁶,Agalbato Cecilia⁶,Munechika Jiro⁷,Matsumoto Hidenari⁷,Menè Roberto⁸,Parati Gianfranco⁸,Cernigliaro Franco⁸,Nerlekar Nitesh⁵,Torlasco Camilla⁸,Pontone Gianluca⁶,Dey Damini¹,Slomka Piotr¹

Affiliation:

1. Cedars-Sinai Medical Center, Department of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Los Angeles, California, United States

2. Medical University of Warsaw, Warsaw, Poland

3. Semmelweis University, Budapest, Hungary

4. Azienda Ospedaliero-Universitaria di Parma, Parma, Italy

5. Monash Health, Melbourne, Victoria, Australia

6. University of Milan, Centro Cardiologico Monzino IRCCS, Milan, Italy

7. Showa University School of Medicine, Tokyo, Japan

8. IRCCS Istituto Auxologico Italiano, Department of Cardiovascular, Neural and Metabolic Sciences, Milan, Italy

Funder

National Heart, Lung, and Blood Institute

Publisher

SPIE-Intl Soc Optical Eng

Subject