Abstract：
Pathologic diagnosis of flat urothelial lesions is subject to high interobserver variability. We expected that deep learning could improve the accuracy and consistency of such pathologic diagnosis, although the learning process is a black box. We therefore propose a new approach for pathologic image classification incorporating the diagnostic process of the pathologist into a deep learning method.
A total of 267 H&E-stained slides of normal urothelium and urothelial lesions from 127 cases were examined. Six independent convolutional neural networks were trained to classify pathologic images according to six pathologic criteria. We then used these networks in the main training for the final diagnosis.
Compared with conventional manual analysis, our method significantly improved the classification accuracy of images of flat urothelial lesions. The automated classification showed almost perfect agreement (weighted κ = 0.98) with the consensus reading. In addition, our approach provides the advantages of reliable diagnosis corresponding to histologic interpretation.
We used deep learning to establish an automated subtype classifier for flat urothelial lesions that successfully combines traditional morphologic approaches and complex deep learning to achieve a learning mechanism that seems plausible to the pathologist.
A total of 267 H&E-stained slides of normal urothelium and urothelial lesions from 127 cases were examined. Six independent convolutional neural networks were trained to classify pathologic images according to six pathologic criteria. We then used these networks in the main training for the final diagnosis.
Compared with conventional manual analysis, our method significantly improved the classification accuracy of images of flat urothelial lesions. The automated classification showed almost perfect agreement (weighted κ = 0.98) with the consensus reading. In addition, our approach provides the advantages of reliable diagnosis corresponding to histologic interpretation.
We used deep learning to establish an automated subtype classifier for flat urothelial lesions that successfully combines traditional morphologic approaches and complex deep learning to achieve a learning mechanism that seems plausible to the pathologist.
摘要：
平坦尿路上皮病变的病理诊断具有高度的观察者间变异性。我们期望深度学习可以提高这种病理诊断的准确性和一致性。虽然学习过程是一个黑匣子。因此，我们提出了一种新的病理图像分类方法，将病理学家的诊断过程纳入深度学习方法。
共检查了127例正常尿路上皮和尿路上皮病变的267张H&E染色载玻片。训练六个独立的卷积神经网络以根据六个病理标准对病理图像进行分类。然后，我们在最终诊断的主要训练中使用了这些网络。
与传统的手工分析相比，我们的方法显着提高了平坦尿路上皮病变图像的分类精度。自动分类显示与共识读数几乎完美一致(加权κ=0.98)。此外,我们的方法提供了与组织学解释相对应的可靠诊断的优势.
我们使用深度学习为平坦的尿路上皮病变建立了自动亚型分类器，该分类器成功地结合了传统的形态学方法和复杂的深度学习，以实现病理学家似乎合理的学习机制。
共检查了127例正常尿路上皮和尿路上皮病变的267张H&E染色载玻片。训练六个独立的卷积神经网络以根据六个病理标准对病理图像进行分类。然后，我们在最终诊断的主要训练中使用了这些网络。
与传统的手工分析相比，我们的方法显着提高了平坦尿路上皮病变图像的分类精度。自动分类显示与共识读数几乎完美一致(加权κ=0.98)。此外,我们的方法提供了与组织学解释相对应的可靠诊断的优势.
我们使用深度学习为平坦的尿路上皮病变建立了自动亚型分类器，该分类器成功地结合了传统的形态学方法和复杂的深度学习，以实现病理学家似乎合理的学习机制。
