Abstract
Fundus image quality is crucial for screening various ophthalmic diseases. In this paper, we proposed and validated a novel fundus image enhancement method, named importance-guided semi-supervised contrastive constraining (I-SECRET). Specifically, our semi-supervised framework consists of an unsupervised component, a supervised component, and an importance estimation component. The unsupervised part makes use of a large publicly-available dataset of unpaired high-quality and low-quality images via contrastive constraining, whereas the supervised part utilizes paired images through degrading pre-selected high-quality images. The importance estimation provides a pixel-wise importance map to guide both unsupervised and supervised learning. Extensive experiments on both authentic and synthetic data identify the superiority of our proposed method over existing state-of-the-art ones, both quantitatively and qualitatively.
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Cheng, P., Lin, L., Huang, Y., Lyu, J., Tang, X. (2021). I-SECRET: Importance-Guided Fundus Image Enhancement via Semi-supervised Contrastive Constraining. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12908. Springer, Cham. https://doi.org/10.1007/978-3-030-87237-3_9
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