Stanford researchers are leveraging GPU-based machines in the Amazon EC2 cloud to run deep learning workloads with the goal of improving diagnostics for a chronic eye disease, called diabetic retinopathy. The disease is a complication of diabetes that can lead to blindness if blood sugar is poorly controlled. It affects about 45 percent of diabetics and 100 million people worldwide, many in developing nations.
Their approach relies on a convolutional neural net (CNN) that grades the severity of diabetic retinopathy disease states into five categories: 0-4, with 0 being normal and 4 being the most severe.
The researchers have been training their model with a data set of 80,000 images from EYEPACS, a web-based application for exchanging eye-related clinical information, run by the California Health Foundation.
There is a 63 page presentation on Automatic Grading of Eye Diseases Through Deep Learning, 2016
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