Abstract
Diabetic retinopathy (DR) is a common ocular disease in diabetic patients. In DR analysis, doctors first need to select excellent-quality images of ultra wide optical coherence tomography imaging (UW-OCTA). Only high-quality images can be used for lesion segmentation and proliferative diabetic retinopathy (PDR) detection. In practical applications, UW-OCTA has a small number of images with poor quality, so the dataset constructed from UW-OCTA faces the problem of class-imbalance. In this work, we employ data enhancement strategy and develop a loss function to alleviate class-imbalance. Specifically, we apply Fourier Transformation to the poor quality data with limited numbers, thus expanding this category data. We also utilize characteristics of class-imbalance to improve the cross-entropy loss by weighting. This method is evaluated on DRAC2022 dataset, we achieved Quaratic Weight Kappa of 0.7647 and AUC of 0.8458, respectively.
Z. Wu and Y. Chen—The two authors have equal contributions to the paper.
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Acknowledgements
Research supported by National Natural Science Foundation of China (62271149) and Fujian Provincial Natural Science Foundation project (2021J02019).
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Wu, Z., Chen, Y., Zhang, X., Huang, L. (2023). Data Augmentation by Fourier Transformation for Class-Imbalance: Application to Medical Image Quality Assessment. 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_15
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