B-mode ultrasound (BUS) imaging is a routine tool for diagnosis of liver cancers, while contrast-enhanced ultrasound (CEUS) provides additional information to BUS on the local tissue vascularization and perfusion to promote diagnostic accuracy. In this work, we propose to improve the BUS-based computer aided diagnosis for liver cancers by transferring knowledge from the multi-view CEUS images, including the arterial phase, portal venous phase, and delayed phase, respectively. To make full use of the shared labels of paired of BUS and CEUS images to guide knowledge transfer, support vector machine plus (SVM+), a specifically designed transfer learning (TL) classifier for paired data with shared labels, is adopted for this supervised TL. A nonparallel hyperplane based SVM+ (NHSVM+) is first proposed to improve the TL performance by transferring the per-class knowledge from source domain to the corresponding target domain. Moreover, to handle the issue of multi-source TL, a multi-kernel learning based NHSVM+ (MKL-NHSVM+) algorithm is further developed to effectively transfer multi-source knowledge from multi-view CEUS images. The experimental results indicate that the proposed MKL-NHSVM+ outperforms all the compared algorithms for diagnosis of liver cancers, whose mean classification accuracy, sensitivity, and specificity are 88.183.16%, 86.984.77%, and 89.423.77%, respectively.

中文翻译：

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通过多核支持向量机Plus进行多源转移学习，用于基于B型超声的肝癌计算机辅助诊断。

B型超声（BUS）成像是诊断肝癌的常规工具，而对比增强超声（CEUS）为BUS提供有关局部组织血管生成和灌注的其他信息，以提高诊断的准确性。在这项工作中，我们建议通过从多视图CEUS图像中转移知识（包括动脉期，门静脉期和延迟期）来转移知识，从而改进基于BUS的肝癌计算机辅助诊断。为了充分利用成对的BUS和CEUS图像的共享标签来指导知识转移，为此受监督的TL采用了支持向量机plus（SVM +），这是一种专门设计的用于带有共享标签的成对数据的转移学习（TL）分类器。 。首先提出了一种基于非并行超平面的SVM +（NHSVM +），通过将每类知识从源域转移到相应的目标域来提高TL性能。此外，为了解决多源TL的问题，进一步开发了基于多核学习的NHSVM +（MKL-NHSVM +）算法，以有效地从多视图CEUS图像中转移多源知识。实验结果表明，所提出的MKL-NHSVM +优于所有比较的肝癌诊断算法，其平均分类准确性，敏感性和特异性分别为88.183.16％，86.984.77％和89.423.77％。进一步开发了基于多核学习的NHSVM +（MKL-NHSVM +）算法，以有效地从多视图CEUS图像中转移多源知识。实验结果表明，所提出的MKL-NHSVM +优于所有比较的肝癌诊断算法，其平均分类准确性，敏感性和特异性分别为88.183.16％，86.984.77％和89.423.77％。进一步开发了基于多核学习的NHSVM +（MKL-NHSVM +）算法，以有效地从多视图CEUS图像中转移多源知识。实验结果表明，所提出的MKL-NHSVM +优于所有比较的肝癌诊断算法，其平均分类准确性，敏感性和特异性分别为88.183.16％，86.984.77％和89.423.77％。