Zobrazeno 1 - 3
of 3
pro vyhledávání: '"Joseph Enguehard"'
Publikováno v:
IEEE Access, Vol 7, Pp 11093-11104 (2019)
Deep neural networks usually require large labeled datasets to construct accurate models; however, in many real-world scenarios, such as medical image segmentation, labeling data are a time-consuming and costly human (expert) intelligent task. Semi-s
Externí odkaz:
https://doaj.org/article/9293c03edf80431e90db89a31c28e21a
Autor:
Amir A. Amini, Michael F. McNitt-Gray, Keyvan Farahani, Yoganand Balagurunathan, Paul F. Pinsky, Gustavo Perez, Laura Alexandra Daza, Sandy Napel, Jayashree Kalpathy-Cramer, M. Mehdi Farhangi, Lubomir M. Hadjiiski, Alireza Mehrtash, Wiem Safta, Ali Gholipour, Joseph Enguehard, Ehwa Yang, Ricard Delgado-Gonzalo, Aditya Bagari, Renkun Ni, Benjamin Veasey, Kiran Vaidhya, Tina Kapur, Jung Won Moon, Hichem Frigui, Laura Silvana Castillo, Gabriel Bernardino, Pablo Arbeláez, Dmitry B. Goldgof, Xue Feng, Andrew Beers
Publikováno v:
IEEE Trans Med Imaging
IEEE transactions on medical imaging, vol 40, iss 12
IEEE transactions on medical imaging, vol 40, iss 12
Lung cancer is by far the leading cause of cancer death in the US. Recent studies have demonstrated the effectiveness of screening using low dose CT (LDCT) in reducing lung cancer related mortality. While lung nodules are detected with a high rate of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::934c16463d26ea1a8a0252428eb87f02
https://europepmc.org/articles/PMC9531053/
https://europepmc.org/articles/PMC9531053/
Publikováno v:
IEEE Access, Vol 7, Pp 11093-11104 (2019)
Deep neural networks usually require large labeled datasets to construct accurate models; however, in many real-world scenarios, such as medical image segmentation, labeling data are a time-consuming and costly human (expert) intelligent task. Semi-s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::427693ede82b8237256a9e63aa309c32
https://doi.org/10.1109/access.2019.2891970
https://doi.org/10.1109/access.2019.2891970