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:2019,28(5):18-27
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基于深度迁移学习的大鼠肝纤维化诊断
(华南师范大学 计算机学院, 广州 510631)
Diagnosis of Rat Liver Fibrosis Based on Deep Transfer Learning
(School of Computer Science, South China Normal University, Guangzhou 510631, China)
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投稿时间:2018-11-30    修订日期:2018-12-18
中文摘要: 针对肝纤维化临床诊断方法具有有创性和传统机器学习方法特征提取的不完全性的缺陷,本文采用深度迁移学习方法利用预训练的ResNet-18和VGGNet-11模型用于肝纤维化分期诊断.使用南方医科大学提供的大鼠肝纤维化核磁共振影像数据集进行不同程度的迁移训练.将两种模型在通过4种不同参数采集的核磁共振影像数据集上,分别使用6种网络迁移配置训练.实验结果表明,使用T1RHO-FA参数采集的核磁共振影像和采用VGGNet-11模型更能提高肝纤维化分期诊断的准确率.同时相对于ResNet-18模型,深度模型迁移学习方法能稳定提升VGGNet-11模型进行肝纤维化分期诊断的准确率和训练速度.
Abstract:In view of the incompleteness of the clinical diagnosis method of liver fibrosis and the incompleteness of the feature extraction of traditional machine learning methods, by the deep transfer learning method, this study uses the pre-trained ResNet-18 and VGGNet-11 models for the diagnosis of liver fibrosis. Different degrees of transfer training were performed using the rat liver fibrosis nuclear magnetic resonance image dataset provided by Southern Medical University. The two models were trained using six network migration configurations on the MRI image datasets collected by four different parameters. The experimental results show that the use of T1RHO-FA parameters to acquire nuclear magnetic resonance images and the use of VGGNet-11 model can improve the accuracy of liver fibrosis staging diagnosis. At the same time, compared with the ResNet-18 model, the deep model migration learning method can stably improve the accuracy and training speed of the VGGNet-11 model for liver fibrosis staging diagnosis.
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余文林,陈振洲,范冰冰,黄穗.基于深度迁移学习的大鼠肝纤维化诊断.计算机系统应用,2019,28(5):18-27
YU Wen-Lin,CHEN Zhen-Zhou,FAN Bing-Bing,HUANG Sui.Diagnosis of Rat Liver Fibrosis Based on Deep Transfer Learning.COMPUTER SYSTEMS APPLICATIONS,2019,28(5):18-27

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