论文信息 - Deep learning in glaucoma: progress, but still lots to do. - 字舞流文

Deep learning in glaucoma: progress, but still lots to do.

Felipe A. Medeiros | F. Medeiros

[1] Suria S. Mannil,et al. Detection of glaucomatous optic neuropathy with spectral-domain optical coherence tomography: a retrospective training and validation deep-learning analysis. , 2019, The Lancet. Digital health.

[2] K. Park,et al. Macular imaging by optical coherence tomography in the diagnosis and management of glaucoma , 2017, British Journal of Ophthalmology.

[3] Christopher Bowd,et al. Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs , 2018, Scientific Reports.

[4] Felipe A. Medeiros,et al. From Machine to Machine: An OCT-trained Deep Learning Algorithm for Objective Quantification of Glaucomatous Damage in Fundus Photographs , 2018, Ophthalmology.

[5] Miika Linna,et al. Cost effectiveness and cost utility of an organized screening programme for glaucoma. , 2007, Acta ophthalmologica Scandinavica.

[6] M. He,et al. Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs. , 2018, Ophthalmology.

[7] Robert N Weinreb,et al. Estimating Lead Time Gained by Optical Coherence Tomography in Detecting Glaucoma before Development of Visual Field Defects. , 2015, Ophthalmology.

[8] Jian Sun,et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).