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• W3185542499 abstract "Computer-aided diagnosis using retinal fundus images is crucial for the early detection of many ocular and systemic diseases. Nowadays, deep learning-based approaches are commonly used for this purpose. However, training deep neural networks usually requires a large amount of annotated data, which is not always available. In practice, this issue is commonly mitigated with different techniques, such as data augmentation or transfer learning. Nevertheless, the latter is typically faced using networks that were pre-trained on additional annotated data. An emerging alternative to the traditional transfer learning source tasks is the use of self-supervised tasks that do not require manually annotated data for training. In that regard, we propose a novel self-supervised visual learning strategy for improving the retinal computer-aided diagnosis systems using unlabeled multimodal data. In particular, we explore the use of a multimodal reconstruction task between complementary retinal imaging modalities. This allows to take advantage of existent unlabeled multimodal data in the medical domain, improving the diagnosis of different ocular diseases with additional domain-specific knowledge that does not rely on manual annotation. To validate and analyze the proposed approach, we performed several experiments aiming at the diagnosis of different diseases, including two of the most prevalent impairing ocular disorders: glaucoma and age-related macular degeneration. Additionally, the advantages of the proposed approach are clearly demonstrated in the comparisons that we perform against both the common fully-supervised approaches in the literature as well as current self-supervised alternatives for retinal computer-aided diagnosis. In general, the results show a satisfactory performance of our proposal, which improves existing alternatives by leveraging the unlabeled multimodal visual data that is commonly available in the medical field. • Self-supervised multimodal pre-training improves retinal computer-aided diagnosis. • Multimodal reconstruction provides domain-specific knowledge using unlabeled images. • Age-related macular degeneration and glaucoma diagnosis using deep learning. • Deep learning approach for computer-aided diagnosis with limited annotated data. • The proposal outperforms other self-supervised and fully-supervised alternatives." @default.
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• W3185542499 date "2021-12-01" @default.
• W3185542499 modified "2023-10-10" @default.
• W3185542499 title "Self-supervised multimodal reconstruction pre-training for retinal computer-aided diagnosis" @default.
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• W3185542499 doi "https://doi.org/10.1016/j.eswa.2021.115598" @default.
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