Image Analytics Techniques for Diabetic Retinopathy Detection
Diabetic Retinopathy is the leading cause of blindness in working- age adults. This work aims at enhancing lesion detection, reinforcing referral decisions, and integrating our solutions with low-cost retinal imaging devices. For lesion detection, we proposed a novel coding technique robust to any kind oflesion. For referral decisions, we designed a robust method that does not rely upon lesion detection, proposed an effective data-driven model that significantly improves the performance, designed an accountable model that produces a re- liable response and enables pixel-based importance comprehension, and create local descriptors that are encoded into a rich mid-level representation. Our work has invaluable impacts both in biomedical and technical contexts.
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Pires, R., Avila, S., Wainer, J., Valle, E., Abr`amoff, M. D., and Rocha, A. (2019a). A data-driven approach to referable diabetic retinopathy detection. Artificial Intelligence in Medicine, 96:93–106.
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Pires, R., Ferreira, A., Avila, S., Wainer, J., and Rocha, A. (2019b). An accountable saliency-oriented data-driven approach to diabetic retinopathy detection. In Elsevier, editor, Photo Acoustic and Optical Coherence Tomography Imaging: An Application in Ophthalmology, chapter 10. Elsevier.
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