Classification of optical coherence tomography images using a capsule network

[1] Chan M(2014)Global report on diabetes World Health Organ 58 1-88

[2] Varma R(2014)Prevalence of and risk factors for diabetic macular edema in the United States JAMA Ophthalmol 132 1334-e116

[3] Bressler NM(2014)Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis Lancet Glob Heal 2 e106-12

[4] Doan QV(2017)The ecosystem that powered the translation of OCT from fundamental research to clinical and commercial impact [invited] Biomed Opt Express 8 1638-16

[5] Gleeson M(2018)Deep learning in chest radiography: detection of findings and presence of change PLoS One 13 1-21

[6] Danese M(2018)Deep learning-based quantification of abdominal fat on magnetic resonance images PLoS One 13 1-17

[7] Bower JK(2017)Deep reconstruction model for dynamic PET images PLoS One 12 1-1131

[8] Wong WL(2018)Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study PLoS Med 15 1-618

[9] Su X(2018)Fundus images analysis using deep features for detection of exudates, hemorrhages and microaneurysms BMC Ophthalmol 18 288-6793

[10] Li X(2019)Accuracy of machine learning for differentiation between optic neuropathies and pseudopapilledema BMC Ophthalmol 19 178-742

[1] Chan M(2014)Global report on diabetes World Health Organ 58 1-88

[2] Varma R(2014)Prevalence of and risk factors for diabetic macular edema in the United States JAMA Ophthalmol 132 1334-e116

[3] Bressler NM(2014)Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis Lancet Glob Heal 2 e106-12

[4] Doan QV(2017)The ecosystem that powered the translation of OCT from fundamental research to clinical and commercial impact [invited] Biomed Opt Express 8 1638-16

[5] Gleeson M(2018)Deep learning in chest radiography: detection of findings and presence of change PLoS One 13 1-21

[6] Danese M(2018)Deep learning-based quantification of abdominal fat on magnetic resonance images PLoS One 13 1-17

[7] Bower JK(2017)Deep reconstruction model for dynamic PET images PLoS One 12 1-1131

[8] Wong WL(2018)Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study PLoS Med 15 1-618

[9] Su X(2018)Fundus images analysis using deep features for detection of exudates, hemorrhages and microaneurysms BMC Ophthalmol 18 288-6793

[10] Li X(2019)Accuracy of machine learning for differentiation between optic neuropathies and pseudopapilledema BMC Ophthalmol 19 178-742