CEUS-based classification of liver tumors with deep canonical correlation analysis and multi-kernel learning

The contrast-enhanced ultrasound (CEUS) has been a widely accepted imaging modality for diagnosis of liver cancers. In clinical practice, several typical images selected from enhancement patterns of the arterial, portal venous and late phases can provide reliable information basis for diagnosis. In this work, we propose to develop a CEUS-based computer-aided diagnosis (CAD) for liver cancers with only three typical CEUS images selected from three phases, which simulates the clinical diagnosis mode of radiologists. In the proposed CAD, the deep canonical correlation analysis (DCCA) is first performed on three CEUS pairs between arterial and portal venous phases, arterial and late phases, respectively, due to the effectiveness of multi-view fusion of DCCA. The generated six-view features are then fed to a multiple kernel learning (MKL) classifier to further promote the predictive diagnosis result. The experimental results indicate that the proposed DCCA-MKL algorithm achieves best performance for discriminating benign liver tumors from malignant liver cancers.