Abstract
The diabetic retinopathy (DR) is an eye abnormality that highly causes blindness or affects majority of patients with a history of 15 years of diabetes at least. To diagnose DR, image quality assessment, lesion segmentation, and DR grade classification are required. However, any automatic DR analysis has not been developed yet. Therefore, the challenge DRAC 2022 suggested three tasks; Task 1: Segmentation of Diabetic Retinopathy Lesions; Task 2: Image Quality Assessment; Task 3: Diabetic Retinopathy Grading. These tasks aim to be built robust but adaptable model for automatic DR diagnosis with provided OCT angiography (OCTA) dataset. In this paper, we proposed an automatic DR diagnosis method with deep learning benchmarking, and image processing from OCTA. The proposed method achieved Dice of 0.6046, Cohen kappa of 0.8075, and 0.8902 for each task respectively with the second place ranking in the competition. The code is available at https://github.com/KT-biohealth/DRAC22.
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Choi, S. et al. (2023). A Vision Transformer Based Deep Learning Architecture for Automatic Diagnosis of Diabetic Retinopathy in Optical Coherence Tomography Angiography. In: Sheng, B., Aubreville, M. (eds) Mitosis Domain Generalization and Diabetic Retinopathy Analysis. MIDOG DRAC 2022 2022. Lecture Notes in Computer Science, vol 13597. Springer, Cham. https://doi.org/10.1007/978-3-031-33658-4_13
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