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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Anantrasirichai, N., et al.: Adaptive-weighted bilateral filtering and other pre-processing techniques for optical coherence tomography. Comput. Med. Imagining Graph. 38(6), 526–539 (2014)
OpenCV Document. https://docs.opencv.org
Bao, H., Dong, L., Piao, S., Wei, F.: BEiT: BERT pre-training of image transformers. arXiv preprint (2021)
Lee, D.-H.: Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks. In: Workshop on Challenges in Representation Learning, ICML, vol. 3. no. 2 (2013)
de Barros Garcia, J.M.B., Isaac, D.L.C., Avila, M.: Diabetic retinopathy and OCT angiography: clinical findings and future perspectives. Int. J. Retin. Vitr. 3(1), 1–10 (2017)
Vaz-Pereira, S., Morais-Sarmento, T., Esteves Marques, R.: Optical coherence tomography features of neovascularization in proliferative diabetic retinopathy: a systematic review. Int. J. Retin. Vitr. 6 (2020). Article number: 26. https://doi.org/10.1186/s40942-020-00230-3
Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convent for the 2020s. arXiv (2022). arXiv:2201.03545
Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, M.J., Luo, P.: SegFormer: simple and efficient design for semantic segmentation with transformers. arXiv (2021). arXiv:2105.15203
Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. arXiv (2021). arXiv:2103.14030
Zuiderveld, K.: Contrast limited adaptive histogram equalization. In: Graphics Gems IV, pp. 474–485 (1994)
Dai, L., Wu, L., Li, H., et al.: A deep learning system for detecting diabetic retinopathy across the disease spectrum. Nat. Commun. 12(1), 1–11 (2021)
Liu, R., Wang, X., Wu, Q., et al.: DeepDRiD: diabetic retinopathy-grading and image quality estimation challenge. Patterns 3(6), 100512 (2022)
Sheng, B., et al.: An overview of artificial intelligence in diabetic retinopathy and other ocular diseases. Front. Public Health 10, 971943 (2022). https://doi.org/10.3389/fpubh.2022.971943
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-031-33658-4_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-33657-7
Online ISBN: 978-3-031-33658-4
eBook Packages: Computer ScienceComputer Science (R0)
