Abstract
Myopia is a high-incidence disease that widely exists across various regions. If left unaddressed, it may escalate into high myopia. The leading cause of visual impairment is myopic maculopathy. Currently, certain deep-learning techniques have been employed for the analysis of images depicting myopic maculopathy in fundus photography. These methods are dedicated to assisting physicians in efficient disease diagnosis. In our work, a deep learning framework is introduced to classify images of five different severities of myopic maculopathy. First, we employ a diffusion model to generate a series of images for data augmentation to alleviate the pressure of uneven distribution of categories in training datasets, then we divide images into multiple patches and perform self-supervised learning to generate patch-level feature embeddings. Building upon the above foundation, an aggregator is proposed based on multiple instance learning to achieve image-level classification. We demonstrate the effectiveness of this method in four sufficient experiments with three key evaluation metrics of quadratic-weighted kappa, F1 score, and specificity. Our approach secured the tenth position in the Myopic Maculopathy Analysis Challenge 2023 (MICCAI MMAC 2023).
J. Li, J. Soon and Q. Zhang—Contributed equally.
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Acknowledgements
The work was supported in part by the National Science Foundation of China (NSFC) under Grant 61975089; in part by the grant from the Shenzhen Science and Technology Innovation Commission (Number: KCXFZ20201221173207022, WDZC2020200821141349001, JCYJ20200109110606054). The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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Li, J., Soon, J., Zhang, Q., Zhang, Q., He, Y. (2024). Classification of Myopic Maculopathy Images with Self-supervised Driven Multiple Instance Learning Network. In: Sheng, B., Chen, H., Wong, T.Y. (eds) Myopic Maculopathy Analysis. MICCAI 2023. Lecture Notes in Computer Science, vol 14563. Springer, Cham. https://doi.org/10.1007/978-3-031-54857-4_9
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