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• W4364382904 abstract "Failure to recognize samples from the classes unseen during training is a major limitation of artificial intelligence in the real-world implementation for recognition and classification of retinal anomalies. We established an uncertainty-inspired open-set (UIOS) model, which was trained with fundus images of 9 retinal conditions. Besides assessing the probability of each category, UIOS also calculated an uncertainty score to express its confidence. Our UIOS model with thresholding strategy achieved an F1 score of 99.55%, 97.01% and 91.91% for the internal testing set, external target categories (TC)-JSIEC dataset and TC-unseen testing set, respectively, compared to the F1 score of 92.20%, 80.69% and 64.74% by the standard AI model. Furthermore, UIOS correctly predicted high uncertainty scores, which would prompt the need for a manual check in the datasets of non-target categories retinal diseases, low-quality fundus images, and non-fundus images. UIOS provides a robust method for real-world screening of retinal anomalies." @default.
• W4364382904 created "2023-04-12" @default.
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• W4364382904 date "2023-04-08" @default.
• W4364382904 modified "2023-10-16" @default.
• W4364382904 title "Uncertainty-inspired Open Set Learning for Retinal Anomaly Identification" @default.
• W4364382904 doi "https://doi.org/10.48550/arxiv.2304.03981" @default.
• W4364382904 hasPublicationYear "2023" @default.
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