Deep-learning-based AI for evaluating estimated nonperfusion areas requiring further examination in ultra-widefield fundus images

We herein propose a PraNet-based deep-learning model for estimating the size of non-perfusion area (NPA) in pseudo-color fundus photos from an ultra-wide-field (UWF) image. We trained the model with focal loss and weighted binary cross-entropy loss to deal with the class-imbalanced dataset, and opti...

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Detalles Bibliográficos
Autores principales: Inoda, Satoru, Takahashi, Hidenori, Yamagata, Hitoshi, Hisadome, Yoichiro, Kondo, Yusuke, Tampo, Hironobu, Sakamoto, Shinichi, Katada, Yusaku, Kurihara, Toshihide, Kawashima, Hidetoshi, Yanagi, Yasuo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9759556/
https://www.ncbi.nlm.nih.gov/pubmed/36528737
http://dx.doi.org/10.1038/s41598-022-25894-9

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9759556/
https://www.ncbi.nlm.nih.gov/pubmed/36528737
http://dx.doi.org/10.1038/s41598-022-25894-9

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