Automated system for diagnosing endometrial cancer by adopting deep-learning technology in hysteroscopy

Endometrial cancer is a ubiquitous gynecological disease with increasing global incidence. Therefore, despite the lack of an established screening technique to date, early diagnosis of endometrial cancer assumes critical importance. This paper presents an artificial-intelligence-based system to dete...

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Detalhes bibliográficos
Publicado no:PLoS One
Main Authors: Takahashi, Yu, Sone, Kenbun, Noda, Katsuhiko, Yoshida, Kaname, Toyohara, Yusuke, Kato, Kosuke, Inoue, Futaba, Kukita, Asako, Taguchi, Ayumi, Nishida, Haruka, Miyamoto, Yuichiro, Tanikawa, Michihiro, Tsuruga, Tetsushi, Iriyama, Takayuki, Nagasaka, Kazunori, Matsumoto, Yoko, Hirota, Yasushi, Hiraike-Wada, Osamu, Oda, Katsutoshi, Maruyama, Masanori, Osuga, Yutaka, Fujii, Tomoyuki
Formato: Artigo
Idioma:Inglês
Publicado em: Public Library of Science 2021
Assuntos:
Research Article
Acesso em linha:https://ncbi.nlm.nih.gov/pmc/articles/PMC8011803/
https://ncbi.nlm.nih.gov/pubmed/33788887
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1371/journal.pone.0248526
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