Zobrazeno 1 - 2
of 2
pro vyhledávání: '"Wehrend, John"'
Autor:
Silosky, Michael, Xing, Fuyong, Wehrend, John, Litwiller, Daniel V, Metzler, Scott D, Chin, Bennett B
Publikováno v:
Am J Nucl Med Mol Imaging
Background: Deep learning (DL) algorithms have shown promise in identifying and quantifying lesions in PET/CT. However, the accuracy and generalizability of these algorithms relies on large, diverse datasets which are time and labor intensive to cura
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::aebd708ee32c00a74ef87e4be1ae474c
https://europepmc.org/articles/PMC10009466/
https://europepmc.org/articles/PMC10009466/
Autor:
Silosky M; Department of Radiology, University of Colorado School of Medicine, Anschutz Medical Campus Aurora, CO, USA., Xing F; Department of Biostatistics and Informatics, University of Colorado School of Medicine, Anschutz Medical Campus Aurora, CO, USA., Wehrend J; Department of Radiology, University of Colorado School of Medicine, Anschutz Medical Campus Aurora, CO, USA., Litwiller DV; GE Healthcare Denver, CO, USA., Metzler SD; Department of Radiology, Perelman School of Medicine, University of Pennsylvania Philadelphia, PA, USA., Chin BB; Department of Radiology, University of Colorado School of Medicine, Anschutz Medical Campus Aurora, CO, USA.
Publikováno v:
American journal of nuclear medicine and molecular imaging [Am J Nucl Med Mol Imaging] 2023 Feb 15; Vol. 13 (1), pp. 33-42. Date of Electronic Publication: 2023 Feb 15 (Print Publication: 2023).
