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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Detalles Bibliográficos
Autores principales: 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: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8011803/
https://www.ncbi.nlm.nih.gov/pubmed/33788887
http://dx.doi.org/10.1371/journal.pone.0248526

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8011803/
https://www.ncbi.nlm.nih.gov/pubmed/33788887
http://dx.doi.org/10.1371/journal.pone.0248526

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