Liver Fibrosis Classification Based on Transfer Learning and FCNet for Ultrasound Images
Liver Fibrosis Classification Based on Transfer Learning and FCNet for Ultrasound Images
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基于迁移学习和 FCNet 的超声图像肝纤维化分类
DOI：
10.1109/access.2017.2689058
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发表时间：
2017-01-01
期刊：
影响因子：
3.9
通讯作者：
Hu, Bing
中科院分区：
文献类型：
--
作者：
Meng, Dan;Zhang, Libo;Hu, Bing
诊断超声在诊断准确性和鲁棒性方面提供了很大的改进。然而，很难做出主观和统一的诊断，因为超声图像的质量很容易受到机器设置、超声波的特性、超声与身体组织之间的相互作用以及其他不可控因素的影响。在本文中，我们提出了一种新的基于转移学习（IT）的肝纤维化分类方法，使用VGGNet和一种称为全连接网络（FCNet）的深度分类器。在样本不足的情况下，使用TL策略提取的深度特征可以提供足够的分类信息。然后将这些深度特征发送到FCNet，用于不同肝纤维化状态的分类。通过这个框架，测试表明，与其他方法相比，我们结合FCNet的深度特征可以提供合适的信息，从而构建最准确的预测模型。
Diagnostic ultrasound offers great improvements in diagnostic accuracy and robustness. However, it is difficult to make subjective and uniform diagnoses, because the quality of ultrasound images can be easily influenced by machine settings, the characteristics of ultrasonic waves, the interactions between ultrasound and body tissues, and other uncontrollable factors. In this paper, we propose a novel liver fibrosis classification method based on transfer learning (IT) using VGGNet and a deep classifier called fully connected network (FCNet). In case of insufficient samples, deep features extracted using TL strategy can provide sufficient classification information. These deep features are then sent to FCNet for the classification of different liver fibrosis statuses. With this framework, tests show that our deep features combined with the FCNet can provide suitable information to enable the construction of the most accurate prediction model when compared with other methods.