A two-stage multi-view learning framework based computer-aided diagnosis of liver tumors with contrast enhanced ultrasound images
A two-stage multi-view learning framework based computer-aided diagnosis of liver tumors with contrast enhanced ultrasound images
复制标题
基于两阶段多视图学习框架的超声造影计算机辅助诊断肝脏肿瘤
DOI：
10.3233/ch-170275
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发表时间：
2018-01-01
影响因子：
2.1
通讯作者：
Xu, Hui-Xiong
中科院分区：
文献类型：
--
作者：
Guo, Le-Hang;Wang, Dan;Xu, Hui-Xiong
目的：随着人工智能技术的快速发展，我们提出了一种新的两阶段多视角学习框架，用于基于超声造影（CEUS）的肝脏肿瘤计算机辅助诊断，该框架仅采用动脉期、门静脉期和晚期三种典型的CEUS图像。材料与方法：在第一阶段中，分别对动脉期与门静脉期、动脉期与延迟期、门静脉期与延迟期之间的三个图像对进行深度典型相关分析（DCCA），然后生成总共六个视图特征。而在第二阶段，这些多视图的功能，然后送入一个多核学习（MKL）为基础的分类器，以进一步提高诊断结果。两个MKL分类算法进行了评估，在这个MKL为基础的分类框架。我们在93处病变上评价了建议的DCCA-MKL框架（47例恶性肿瘤对46例良性肿瘤）。所提出的DCCA-MKL框架实现了平均分类准确性、灵敏度、特异性、约登指数、假阳性率和假阴性率分别为90.41 +/-5.80%、93.56 +/-5.90%、86.89 +/-9.38%、结论：DCCA-MKL分类器在区分肝良性肿瘤和恶性肿瘤方面取得了较好的效果。此外，它也证明了基于三维CEUS图像的计算机辅助设计的肝肿瘤与建议DCCA-MKL框架是可行的。
OBJECTIVE: With the fast development of artificial intelligence techniques, we proposed a novel two-stage multi-view learning framework for the contrast-enhanced ultrasound (CEUS) based computer-aided diagnosis for liver tumors, which adopted only three typical CEUS images selected from the arterial phase, portal venous phase and late phase.MATERIALS AND METHODS: In the first stage, the deep canonical correlation analysis (DCCA) was performed on three image pairs between the arterial and portal venous phases, arterial and delayed phases, and portal venous and delayed phases respectively, which then generated total six-view features. While in the second stage, these multi-view features were then fed to a multiple kernel learning (MKL) based classifier to further promote the diagnosis result. Two MKL classification algorithms were evaluated in this MKL-based classification framework. We evaluated proposed DCCA-MKL framework on 93 lesions (47 malignant cancers vs. 46 benign tumors).RESULTS: The proposed DCCA-MKL framework achieved the mean classification accuracy, sensitivity, specificity, Youden index, false positive rate, and false negative rate of 90.41 +/- 5.80%, 93.56 +/- 5.90%, 86.89 +/- 9.38%, 79.44 +/- 11.83%, 13.11 +/- 9.38% and 6.44 +/- 5.90%, respectively, by soft margin MKL classifier.CONCLUSION: The experimental results indicate that the proposed DCCA-MKL framework achieves best performance for discriminating benign liver tumors from malignant liver cancers. Moreover, it is also proved that the three-phase CEUS image based CAD is feasible for liver tumors with the proposed DCCA-MKL framework.