Stratification of gastric cancer risk using a deep neural network

BACKGROUND AND AIM: Stratifying gastric cancer (GC) risk and endoscopy findings in high‐risk individuals may provide effective surveillance for GC. We developed a computerized image‐ analysis system for endoscopic images to stratify the risk of GC. METHODS: The system was trained using images taken...

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Detalles Bibliográficos
Autores principales: Nakahira, Hiroko, Ishihara, Ryu, Aoyama, Kazuharu, Kono, Mitsuhiro, Fukuda, Hiromu, Shimamoto, Yusaku, Nakagawa, Kentaro, Ohmori, Masayasu, Iwatsubo, Taro, Iwagami, Hiroyoshi, Matsuno, Kenshi, Inoue, Shuntaro, Matsuura, Noriko, Shichijo, Satoki, Maekawa, Akira, Kanesaka, Takashi, Yamamoto, Sachiko, Takeuchi, Yoji, Higashino, Koji, Uedo, Noriya, Matsunaga, Takashi, Tada, Tomohiro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Wiley Publishing Asia Pty Ltd 2019
Materias:
Original Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7273725/
https://www.ncbi.nlm.nih.gov/pubmed/32514455
http://dx.doi.org/10.1002/jgh3.12281

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7273725/
https://www.ncbi.nlm.nih.gov/pubmed/32514455
http://dx.doi.org/10.1002/jgh3.12281

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