2 March 2018 Transfer learning for diabetic retinopathy
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Abstract
Diabetic Retinopathy (DR)1, 2 is a leading cause of blindness worldwide and is estimated to threaten the vision of nearly 200 million by 2030.3 To work with the ever-increasing population, the use of image processing algorithms to screen for those at risk has been on the rise. Research-oriented solutions have proven effective in classifying images with or without DR, but often fail to address the true need of the clinic - referring only those who need to be seen by a specialist, and reading every single case. In this work, we leverage an array of image pre-preprocessing techniques, as well as Transfer Learning to re-purpose an existing deep network for our tasks in DR. We train, test, and validate our system on 979 clinical cases, achieving a 95% Area Under the Curve (AUC) for referring Severe DR with an equal error Sensitivity and Specificity of 90%. Our system does not reject any images based on their quality, and is agnostic in terms of eye side and field. These results show that general purpose classifiers can, with the right type of input, have a major impact in clinical environments or for teams lacking access to large volumes of data or high-throughput supercomputers.
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Jeremy Benson, Jeremy Benson, Hector Carrillo, Hector Carrillo, Jeff Wigdahl, Jeff Wigdahl, Sheila Nemeth, Sheila Nemeth, John Maynard, John Maynard, Gilberto Zamora, Gilberto Zamora, Simon Barriga, Simon Barriga, Trilce Estrada, Trilce Estrada, Peter Soliz, Peter Soliz, } "Transfer learning for diabetic retinopathy", Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 105741Z (2 March 2018); doi: 10.1117/12.2293378; https://doi.org/10.1117/12.2293378
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