B-Mode Ultrasound Based Diagnosis of Liver Cancer With CEUS Images as Privileged Information

Contrast-enhanced ultrasound (CEUS) is a valuable imaging modality for diagnosis of liver cancers. However, the complexity of CEUS-based diagnosis limits its wide application, and the B-mode ultrasound (BUS) is still the most popular diagnosis modality in clinical practice. In order to promote BUS-based computer-aided diagnosis (CAD) for liver cancers, we propose a learning using privileged information (LUPI) based CAD with BUS as the diagnosis modality and CEUS as PI. Particularly, the multimodal restricted Boltzmann machine (MRBM) works as a LUPI paradigm. That is, one BUS image and three CEUS images from the arterial phase, portal venous phase and delayed phase, respectively, are used to train three multimodal restricted Boltzmann machine (MRBM) models during training stage, but only the BUS data will be fed to MRBM to generate new feature representation at testing phase. A multiple empirical kernel learning machine (MEKLM) classifier is then performed on three new feature vectors from three MRBM models for classification of liver cancers. The experimental results show that the proposed MRBM-MEKLM algorithm outperforms all the compared algorithms, suggesting the effectiveness of the proposed LUPI-based CAD for liver cancer.