深度学习在主动脉中膜变性病理图像分类中的应用

Application of deep learning in histopathological image classification of aortic medial degeneration

胸主动脉瘤和夹层(TAAD)是严重的心血管疾病之一,而中膜变性(MD)的组织学改变对疾病的诊断及早期干预具有重要的临床意义。针对病理图像的高度复杂性使得MD的诊断过程耗时费力且一致性差的问题,提出了一种基于深度学习的病理图像分类方法,并将其应用于四种MD病变类型以进行性能验证。该方法使用了一种改进的基于GoogLeNet的卷积神经网络模型,首先采用迁移学习来将先验知识应用于TAAD病理图像的表达,然后使用Focal loss和L2正则化来解决数据不平衡问题,从而进一步优化模型性能。实验结果表明,所提模型的平均四分类准确率达到98.78%,表现出较好的泛化性能。可见所提方法可以有效地提升病理学家的诊断效率。

Thoracic Aortic Aneurysm and Dissection(TAAD)is one of the life-threatening cardiovascular diseases,and the histological changes of Medial Degeneration(MD)have important clinical significance for the diagnosis and early intervention of TAAD.Focusing on the issue that the diagnosis of MD is time-consuming and prone to poor consistency because of the great complexity in histological images,a deep learning based classification method of histological images was proposed,and it was applied to four types of MD pathological changes to verify its performance.In the method,an improved Convolutional Neural Network(CNN)model was employed based on the GoogLeNet.Firstly,transfer learning was adopted for applying the prior knowledge to the expression of TAAD histopathological images.Then,Focal loss and L2 regularization were utilized to solve the data imbalance problem,so as to optimize the model performance.Experimental results show that the proposed model is able to achieve the average accuracy of four-class classification of 98.78%,showing a good generalizability.It can be seen that the proposed method can effectively improve the diagnostic efficiency of pathologists.