论文标题
Uatta-ens：不确定性意识到测试时间增强糖尿病性视网膜病变的合奏
UATTA-ENS: Uncertainty Aware Test Time Augmented Ensemble for PIRC Diabetic Retinopathy Detection
论文作者
论文摘要
深层卷积神经网络已成为一种与医生相当的诊断性能分析医学图像的选择方法，包括诊断糖尿病性视网膜病。但是，常用技术是确定性的，因此无法提供预测不确定性的任何估计。量化模型的不确定性对于降低误诊的风险至关重要。可靠的体系结构应得到充分校准，以避免过度自信的预测。为了解决这个问题，我们提出了一个UATTA-INS：5类PIRC糖尿病性视网膜病变分类的不确定性感知测试时间增强集合技术，以产生可靠且完善的预测。
Deep Ensemble Convolutional Neural Networks has become a methodology of choice for analyzing medical images with a diagnostic performance comparable to a physician, including the diagnosis of Diabetic Retinopathy. However, commonly used techniques are deterministic and are therefore unable to provide any estimate of predictive uncertainty. Quantifying model uncertainty is crucial for reducing the risk of misdiagnosis. A reliable architecture should be well-calibrated to avoid over-confident predictions. To address this, we propose a UATTA-ENS: Uncertainty-Aware Test-Time Augmented Ensemble Technique for 5 Class PIRC Diabetic Retinopathy Classification to produce reliable and well-calibrated predictions.
