Uncertainty-inspired Open Set Learning for Retinal Anomaly Identification
This addresses the real-world limitation of AI in retinal disease screening by improving recognition of unseen conditions, though it is incremental as it builds on existing open-set learning methods.
The paper tackled the problem of AI models failing to recognize unseen classes in retinal anomaly identification by developing an uncertainty-inspired open-set model, which achieved F1 scores of up to 99.55% on internal testing and significantly outperformed a standard AI model on external datasets.
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.