Detection of age-related macular degeneration via deep learning

By Philippe M. Burlina, D. E. Freund, N. Joshi, Yulia Wolfson, N. Bressler. 2016

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Opportunities and obstacles for deep learning in biology and medicine (2017, background): “Such features were also repurposed to detect melanoma, the deadliest form of skin cancer, from dermoscopic [47,48] and non-dermoscopic images of skin lesions [4,49,50] as well as age-related macular degeneration [51].”
A survey on deep learning in medical image analysis (2017, methodology): “(2016) Diabetic retinopathy detection; end-to-end CNN, outperforms traditional method, evaluated on a public dataset Burlina et al. (2016) Age-related macular degeneration detection; uses overfeat pretrained network for feature extraction van Grinsven et al.”
Deep image mining for diabetic retinopathy screening (2016, methodology): “Similarly to the AMD detector, image features are extracted with a ConvNet and a multi-class SVM is used to differentiate the various pathologies.”
DeepSeeNet: A deep learning model for automated classification of patient-based age-related macular degeneration severity from color fundus photographs (2018, methodology): “Prior to training, we followed the lead of Burlina et al 18,35 to preprocess our image data as follows: the AREDS fundus photographs were cropped to generate a square image ﬁeld encompassing the macula, followed by scaling the image to a resolution of 224 × 224 pixels (Figure S3).”