论文标题
无监督的不确定性在青光眼筛查中眼底照片的无范围内检测
Deep Dirichlet uncertainty for unsupervised out-of-distribution detection of eye fundus photographs in glaucoma screening
论文作者
论文摘要
使用颜色眼底照片的早期青光眼诊断的自动工具开发可以显着减少该疾病的影响。但是，当前的最新解决方案对现实情况并不强大，这为过度分发案例提供了过度自信的预测。考虑到这一点，我们提出了一个基于Dirichlet分布的模型，该模型允许获得类概率，以及不确定性估计，而无需暴露于分布外情况。我们展示了我们对Airogs挑战的方法。在最终测试阶段开始（2022年2月8日），我们的方法在所有提交中的平均得分最高。
The development of automatic tools for early glaucoma diagnosis with color fundus photographs can significantly reduce 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 Dirichlet 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. At the start of the final test phase (8 Feb. 2022), our method had the highest average score among all submissions.
