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5 March 2021 Progress on combining OCT-A with deep learning for diabetic retinopathy diagnosis
Timothy Yu, Da Ma, Julian Lo, Cyrus WaChong, Michael Chambers, Mirza Faisal Beg, Marinko V. Sarunic
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Proceedings Volume 11630, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXV; 116300Z (2021) https://doi.org/10.1117/12.2578995
Event: SPIE BiOS, 2021, Online Only
Abstract
We present novel approaches of implementing state-of-the-art deep learning techniques for the processing of optical coherence tomography angiography (OCT-A) images for the classification of diabetic retinopathy (DR) severity. The effects of feature-engineering on a deep neural network’s classification performance is compared against unprocessed OCT-A images. We investigate the effects of lower axial resolution (simulated by using a narrower spectral bandwidth) on the classification of DR severity, and the recovery of lost features using a generative adversarial network. We also explore the relationship between DR severity classification and lateral resolution.
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Timothy Yu, Da Ma, Julian Lo, Cyrus WaChong, Michael Chambers, Mirza Faisal Beg, and Marinko V. Sarunic "Progress on combining OCT-A with deep learning for diabetic retinopathy diagnosis", Proc. SPIE 11630, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXV, 116300Z (5 March 2021); https://doi.org/10.1117/12.2578995
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KEYWORDS
Image classification
Angiography
Image processing
Image resolution
Machine learning
Optical coherence tomography
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