Abstract
The performance of disease classification can be improved through improvements in the training process, such as changes in data augmentation, optimization methods, and deep learning model architectures. In the Diabetic Retinopathy Analysis Challenge, we employ a series of techniques to enhance the performance of the diabetic retinopathy grading. In this paper, we examine a collection of these improvements and empirically evaluate their impact on the final model accuracy through experiments. Experiments show that these improvements can significantly improve the performance of the model. For this task, we use a single SeResNext to improve the validation score from 0.8322 to 0.8721.
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Li, R., Gu, Y., Wang, X., Lu, S. (2023). Bag of Tricks for Diabetic Retinopathy Grading of Ultra-Wide Optical Coherence Tomography Angiography Images. 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_3
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DOI: https://doi.org/10.1007/978-3-031-33658-4_3
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