Uncertainty-inspired open set learning for retinal anomaly identification

Failure to recognize samples from the classes unseen during training is a major limitation of artificial intelligence in the real-world implementation for recognition and classification of retinal anomalies. We establish an uncertainty-inspired open set (UIOS) model, which is trained with fundus ima...

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Detalles Bibliográficos
Autores principales: Wang, Meng, Lin, Tian, Wang, Lianyu, Lin, Aidi, Zou, Ke, Xu, Xinxing, Zhou, Yi, Peng, Yuanyuan, Meng, Qingquan, Qian, Yiming, Deng, Guoyao, Wu, Zhiqun, Chen, Junhong, Lin, Jianhong, Zhang, Mingzhi, Zhu, Weifang, Zhang, Changqing, Zhang, Daoqiang, Goh, Rick Siow Mong, Liu, Yong, Pang, Chi Pui, Chen, Xinjian, Chen, Haoyu, Fu, Huazhu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10598011/
https://www.ncbi.nlm.nih.gov/pubmed/37875484
http://dx.doi.org/10.1038/s41467-023-42444-7

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10598011/
https://www.ncbi.nlm.nih.gov/pubmed/37875484
http://dx.doi.org/10.1038/s41467-023-42444-7

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