8 citations · 8 across the 3 of their papers we have counts for
5 papers
Multi-modal, multi-task, multi-attention (M3) deep learning detection of reticular pseudodrusen: towards automated and accessible classification of age-related macular degeneration
Qingyu Chen, Tiarnan D. L. Keenan, Alexis Allot +14
Objective Reticular pseudodrusen (RPD), a key feature of age-related macular degeneration (AMD), are poorly detected by human experts on standard color fundus photography (CFP) and…
Predicting risk of late age-related macular degeneration using deep learning
Yifan Peng, Tiarnan D. Keenan, Qingyu Chen +5
By 2040, age-related macular degeneration (AMD) will affect approximately 288 million people worldwide. Identifying individuals at high risk of progression to late AMD, the sight-t…
A deep learning approach for automated detection of geographic atrophy from color fundus photographs
Tiarnan D. Keenan, Shazia Dharssi, Yifan Peng +5
Purpose: To assess the utility of deep learning in the detection of geographic atrophy (GA) from color fundus photographs; secondary aim to explore potential utility in detecting c…
A multi-task deep learning model for the classification of Age-related Macular Degeneration
Qingyu Chen, Yifan Peng, Tiarnan Keenan +5
Age-related Macular Degeneration (AMD) is a leading cause of blindness. Although the Age-Related Eye Disease Study group previously developed a 9-step AMD severity scale for manual…
DeepSeeNet: A deep learning model for automated classification of patient-based age-related macular degeneration severity from color fundus photographs
Yifan Peng, Shazia Dharssi, Qingyu Chen +5
In assessing the severity of age-related macular degeneration (AMD), the Age-Related Eye Disease Study (AREDS) Simplified Severity Scale predicts the risk of progression to late AM…