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.

中文翻译：

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使用不确定性引导的贝叶斯卷积神经网络对肾级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间一致性低，误诊与患者发病率和死亡率增加有关。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，但很少有人根据肾脏水平狼疮性肾小球肾炎 (LGN) 评分对肾脏进行准确分类。CAD 系统的成功实施也受到诊断医师感知分类器优势和劣势的阻碍，这已被证明对患者结果产生负面影响。我们提出了一种不确定性引导的贝叶斯分类 (UGBC) 方案，旨在准确地对控制、I/II 类、在肾小球水平分类任务（26,634 个分割的肾小球图像）和肾脏水平分类任务（87 MRL/lpr 小鼠肾脏切片）中的 III/IV 类 LGN（3 类）。数据注释是使用高吞吐量、批量标记方案执行的，该方案旨在利用深度神经网络（或 DNN）对标签噪声的抵抗力。我们的增强 UGBC 方案实现了 94.5% 的加权肾小球级精度，同时实现了 96.6% 的加权肾脏级精度，分别比标准卷积神经网络 (CNN) 架构提高了 11.8% 和 3.5%。批量标记方案，旨在利用深度神经网络（或 DNN）对标签噪声的抵抗力。我们的增强 UGBC 方案实现了 94.5% 的加权肾小球级精度，同时实现了 96.6% 的加权肾脏级精度，分别比标准卷积神经网络 (CNN) 架构提高了 11.8% 和 3.5%。批量标记方案，旨在利用深度神经网络（或 DNN）对标签噪声的抵抗力。我们的增强 UGBC 方案实现了 94.5% 的加权肾小球级精度，同时实现了 96.6% 的加权肾脏级精度，分别比标准卷积神经网络 (CNN) 架构提高了 11.8% 和 3.5%。