Deep Dirichlet Uncertainty for Unsupervised Out-of-Distribution Detection of Eye Fundus Photographs in Glaucoma Screening
2022 IEEE International Symposium on Biomedical Imaging Challenges (ISBIC)(2022)
摘要
The development of automatic tools for early glaucoma diagnosis with color fundus photographs can significantly re-duce the impact of this disease. However, current state-of-the-art solutions are not robust to real-world scenarios, providing over-confident predictions for out-of-distribution cases. With this in mind, we propose a model based on the Dirich-let distribution that allows to obtain class-wise probabilities together with an uncertainty estimation without exposure to out-of-distribution cases. We demonstrate our approach on the AIROGS challenge, where we achieve a performance sim-ilar to other participants without requiring additional annotations or artificially generated out-of-distribution labels.
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关键词
glaucoma,deep learning,uncertainty,outlier detection
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