论文标题
超声肝纤维化使用多指导的深度神经网络诊断
Ultrasound Liver Fibrosis Diagnosis using Multi-indicator guided Deep Neural Networks
论文作者
论文摘要
对纤维化阶段的准确分析在慢性乙型肝炎感染患者的随访中起着非常重要的作用。在本文中，为自动肝纤维化预测提供了一个深度学习框架。相反，我们的方法可以利用多个超声图像提供的信息。进一步提出了一种指标指导的学习机制，以减轻所提出模型的训练。这是在临床诊断的工作流程之后，并使预测程序可以解释。为了支持培训，数据集的收集良好，其中包含229名患者的超声视频/图像，指标和标签。正如实验结果所证明的那样，我们提出的模型通过实现最先进的性能来显示其有效性，具体来说，准确性为65.6％（比以前的最佳表现高20％）。
Accurate analysis of the fibrosis stage plays very important roles in follow-up of patients with chronic hepatitis B infection. In this paper, a deep learning framework is presented for automatically liver fibrosis prediction. On contrary of previous works, our approach can take use of the information provided by multiple ultrasound images. An indicator-guided learning mechanism is further proposed to ease the training of the proposed model. This follows the workflow of clinical diagnosis and make the prediction procedure interpretable. To support the training, a dataset is well-collected which contains the ultrasound videos/images, indicators and labels of 229 patients. As demonstrated in the experimental results, our proposed model shows its effectiveness by achieving the state-of-the-art performance, specifically, the accuracy is 65.6%(20% higher than previous best).
