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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...
| Autores principales: | , , , , , , , , , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Nature Publishing Group UK
2021
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| Materias: | |
| 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 |