Uncertainty-inspired Open Set Learning for Retinal Anomaly Identification

Failure to recognize samples from the classes unseen during training is a major limit of artificial intelligence (AI) in real-world implementation of retinal anomaly classification. To resolve this obstacle, we propose an uncertainty-inspired open-set (UIOS) model which was trained with fundus images of 9 common retinal conditions. Besides the probability of each category, UIOS also calculates 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 testing set and non-typical 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 prompted the need for a manual check, in the datasets of rare retinal diseases, low-quality fundus images, and non-fundus images. This work provides a robust method for real-world screening of retinal anomalies.

A Novel Data Segmentation Method for Data-driven Phase Identification

This paper presents a smart meter phase identification algorithm for two cases: meter-phase-label-known and meter-phase-label-unknown. To improve the identification accuracy, a data segmentation method is proposed to exclude data segments that are collected when the voltage correlation between smart meters on the same phase are weakened. Then, using the selected data segments, a hierarchical clustering method is used to calculate the correlation distances and cluster the smart meters. If the phase labels are unknown, a Connected-Triple-based Similarity (CTS) method is adapted to further improve the phase identification accuracy of the ensemble clustering method. The methods are developed and tested on both synthetic and real feeder data sets. Simulation results show that the proposed phase identification algorithm outperforms the state-of-the-art methods in both accuracy and robustness.