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From Machine to Machine: An OCT-trained Deep Learning Algorithm for Objective Quantification of Glaucomatous Damage in Fundus Photographs
PURPOSE: Previous approaches using deep learning algorithms to classify glaucomatous damage on fundus photographs have been limited by the requirement for human labeling of a reference training set. We propose a new approach using quantitative spectral-domain optical coherence tomography (SDOCT) dat...
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| Publicado no: | Ophthalmology |
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| Main Authors: | , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
2018
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6884092/ https://ncbi.nlm.nih.gov/pubmed/30578810 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1016/j.ophtha.2018.12.033 |
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