Transfer learning for diabetic retinopathy

Jeremy Benson; Hector Carrillo; Jeff Wigdahl; Sheila Nemeth; John Maynard; Gilberto Zamora; Simon Barriga; Trilce Estrada; Peter Soliz

doi:10.1117/12.2293378

2 March 2018 Transfer learning for diabetic retinopathy

Jeremy Benson, Hector Carrillo, Jeff Wigdahl, Sheila Nemeth, John Maynard, Gilberto Zamora, Simon Barriga, Trilce Estrada, Peter Soliz

Author Affiliations +

Proceedings Volume 10574, Medical Imaging 2018: Image Processing; 105741Z (2018) https://doi.org/10.1117/12.2293378
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

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, Hector Carrillo, Jeff Wigdahl, Sheila Nemeth, John Maynard, Gilberto Zamora, Simon Barriga, Trilce Estrada, and Peter Soliz "Transfer learning for diabetic retinopathy", Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 105741Z (2 March 2018); https://doi.org/10.1117/12.2293378

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