Kidney Level Lupus Nephritis Classification using Uncertainty Guided Bayesian Co

Kidney Level Lupus Nephritis Classification using Uncertainty Guided Bayesian Convolutional Neural Networks

The kidney biopsy based diagnosis of Lupus Nephritis (LN) is characterized by low inter-observer agreement, with misdiagnosis being associated with increased patient morbidity and mortality. Although various Computer Aided Diagnosis (CAD) systems have been developed for other nephrohistopathological applications, little has been done to accurately classify kidneys based on their kidney level Lupus Glomerulonephritis (LGN) scores.The successful implementation of CAD systems has also been hindered by the diagnosing physician's perceived classifier strengths and weaknesses, which has been shown to have a negative effect on patient outcomes. We propose an Uncertainty-Guided Bayesian Classification (UGBC) scheme that is designed to accurately classify control, class I/II, and class III/IV LGN (3 class) at both the glomerular-level classification task (26,634 segmented glomerulus images) and the kidney-level classification task (87 MRL/lpr mouse kidney sections). Data annotation was performed using a high throughput, bulk labeling scheme that is designed to take advantage of Deep Neural Network's (or DNNs) resistance to label noise. Our augmented UGBC scheme achieved a 94.5% weighted glomerular-level accuracy while achieving a weighted kidney-level accuracy of 96.6%, improving upon the standard Convolutional Neural Network (CNN) architecture by 11.8% and 3.5% respectively.

使用不确定度指导的贝叶斯卷积神经网络对肾脏水平的狼疮性肾炎进行分类

基于肾活检的狼疮性肾炎（LN）诊断的特点是观察者之间的共识度低，误诊与患者发病率和死亡率增加相关。尽管已经为其他肾组织病理学应用开发了各种计算机辅助诊断（CAD）系统，但是根据肾脏的狼疮性肾小球肾炎（LGN）评分对肾脏进行准确分类的工作却很少。.. 诊断医生所感知的分类器的优缺点也阻碍了CAD系统的成功实施，这已被证明对患者预后具有负面影响。我们提出了不确定性贝叶斯分类（UGBC）方案，该方案旨在在肾小球级别分类任务（26,634个分割的肾小球图像）和肾脏级分类任务（87个MRL / lpr小鼠肾脏切片）。数据注释使用高吞吐量的批量标记方案执行，该方案旨在利用深度神经网络（或DNN）的抗标记噪声性能。我们的增强型UGBC方案实现了94.5％的加权肾小球水平准确性，同时实现了96.6％的加权肾脏水平准确性， （阅读更多）