Multimodal Ultrasound imaging based diagnosis of liver cancers with a two-stage multi-view learning framework

Computer-aided diagnosis (CAD) of liver cancers on contrast-enhanced ultrasound (CEUS) has attracted considerable attention in recent years. The enhancement patterns on CEUS for liver lesions consist of the arterial, portal venous and late phases. Several typical images selected from these three phases can provide reliable information basis for diagnosis of liver lesions. Therefore, we propose to develop a CAD framework for liver cancers with only one B-mode image and three typical CEUS images selected from three enhancement patterns, which simulates the clinical diagnosis mode of radiologists. Moreover, a framework of two-stage multi-view learning (TS-MVL) is proposed to perform both feature-level and classifier-level MVL for the diagnosis of liver cancers with multimodal ultrasound images. We propose to apply the nonlinear kernel matrix (NKM) algorithm to effectively fuse the features of multimodal ultrasound images, and then perform the multiple kernel boosting (MKB) algorithm to promote the predictive performance of multiple classifiers according to multi-view features. The experimental results indicate that the proposed algorithm outperforms the commonly used multi-view learning algorithms.