A hierarchical deep learning approach with transparency and interpretability based on small samples for glaucoma diagnosis

The application of deep learning algorithms for medical diagnosis in the real world faces challenges with transparency and interpretability. The labeling of large-scale samples leads to costly investment in developing deep learning algorithms. The application of human prior knowledge is an effective...

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Detalles Bibliográficos
Autores principales: Xu, Yongli, Hu, Man, Liu, Hanruo, Yang, Hao, Wang, Huaizhou, Lu, Shuai, Liang, Tianwei, Li, Xiaoxing, Xu, Mai, Li, Liu, Li, Huiqi, Ji, Xin, Wang, Zhijun, Li, Li, Weinreb, Robert N., Wang, Ningli
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7952384/
https://www.ncbi.nlm.nih.gov/pubmed/33707616
http://dx.doi.org/10.1038/s41746-021-00417-4

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7952384/
https://www.ncbi.nlm.nih.gov/pubmed/33707616
http://dx.doi.org/10.1038/s41746-021-00417-4

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