Polyp characterization using deep learning and a publicly accessible polyp video database

OBJECTIVES: Convolutional neural networks (CNN) for computer‐aided diagnosis of polyps are often trained using high‐quality still images in a single chromoendoscopy imaging modality with sessile serrated lesions (SSLs) often excluded. This study developed a CNN from videos to classify polyps as aden...

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Detalles Bibliográficos
Autores principales: Kader, Rawen, Cid‐Mejias, Anton, Brandao, Patrick, Islam, Shahraz, Hebbar, Sanjith, Puyal, Juana González‐Bueno, Ahmad, Omer F., Hussein, Mohamed, Toth, Daniel, Mountney, Peter, Seward, Ed, Vega, Roser, Stoyanov, Danail, Lovat, Laurence B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2023
Materias:
Original Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10570984/
https://www.ncbi.nlm.nih.gov/pubmed/36527309
http://dx.doi.org/10.1111/den.14500

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10570984/
https://www.ncbi.nlm.nih.gov/pubmed/36527309
http://dx.doi.org/10.1111/den.14500

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