Real-time semantic segmentation of gastric intestinal metaplasia using a deep learning approach

BACKGROUND/AIMS: Previous artificial intelligence (AI) models attempting to segment gastric intestinal metaplasia (GIM) areas have failed to be deployed in real-time endoscopy due to their slow inference speeds. Here, we propose a new GIM segmentation AI model with inference speeds faster than 25 fr...

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Detalles Bibliográficos
Autores principales: Siripoppohn, Vitchaya, Pittayanon, Rapat, Tiankanon, Kasenee, Faknak, Natee, Sanpavat, Anapat, Klaikaew, Naruemon, Vateekul, Peerapon, Rerknimitr, Rungsun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Korean Society of Gastrointestinal Endoscopy 2022
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9178134/
https://www.ncbi.nlm.nih.gov/pubmed/35534933
http://dx.doi.org/10.5946/ce.2022.005

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9178134/
https://www.ncbi.nlm.nih.gov/pubmed/35534933
http://dx.doi.org/10.5946/ce.2022.005

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