Abstract
Quality assessment of ultra-wide optical coherence tomography angiography (UW-OCTA) images followed by lesion segmentation and proliferatived diabetic retinopathy (PDR) detection is of great significance for the diagnosis of diabetic retinopathy. However, due to the complexity of UW-OCTA images, it is challenging to achieve automatic image quality assessment and PDR detection in a limited dataset. This work presented a fully automated convolutional neural network-based method for image quality assessment and retinopathy grading. In the first stage, the dataset was augmented to eliminate the category imbalance problem. In the second stage, the EfficientNet-B2 network, pre-trained on ImageNet, was used for quality assessment and lesion grading of UW-OCTA images. We evaluated our method on the DRAC2022 dataset. A quadratic weighted kappa score of 0.7704 was obtained on the task 2 image quality assessment test set and 0.8029 on the task 3 retinopathy grading test set.
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Chen, Z., Huang, L. (2023). Deep Convolutional Neural Network for Image Quality Assessment and Diabetic Retinopathy Grading. 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_4
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DOI: https://doi.org/10.1007/978-3-031-33658-4_4
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