For the diagnosis of liver cancer, conventional brightness mode (B-mode) can only provide morphological information. Multi-modal ultrasound, including shear-wave elastography (SWE) and contrast enhanced ultrasound (CEUS), can provide comprehensive diagnostic information on tumor microenvironment and tissue perfusion . The challenge is to effectively explore the multi-modal features of ultrasound. Besides, there are many fusion strategies currently available, but there is a lack of systematic comparative research on the various fusion strategies. In this study, we designed 'Lesions Pairing' to construct the dataset, addressing the challenge of small sample sizes in multi-modal learning. We then compared the effectiveness of different strategies and proposed hybrid-fusion strategies based on the combination of conventional layer-level fusion (i.e. early-fusion, mid-fusion and late-fusion), which can efficiently extract intra-/inter- modal information. Specifically, we first systematically compared different deep-learning-based fusion strategies for multi-modal ultrasound in the diagnosis of liver cancer. Secondly, based on the comparison results of a multimodal framework that integrates B-mode, SWE, CEUS ultrasound data, and clinical data simultaneously, we propose a hybrid-fusion strategies for the diagnosis of hepatocellular carcinoma and intrahepatic cholangiocarcinoma . The experimental results showed that the area under the curve of the early-late fusion strategy combined with clinical data was 0.9854, which was superior to other single mode and other fusion strategies, increasing by 13.8–25.88 % and 2.22 %-9.79 %, respectively.

Lesion localization and tracking are critical for accurate, automated medical imaging analysis. Contrast-enhanced ultrasound (CEUS) significantly enriches traditional B-mode ultrasound with contrast agents to provide high-resolution, real-time images of blood flow in tissues and organs. However, many trackers, designed primarily for natural RGB or B-mode ultrasound images, underutilize the extensive data from dual-screen enhanced images and fail to account for respiratory motion, thus facing challenges in achieving accurate target tracking. To address the existing challenges, we propose an adaptive-weighted dual mapping (ADMNet), an online tracking framework tailored for CEUS. Firstly, we introduced a novel Multimodal Atrous Attention Fusion (MAAF) module, innovatively designed to adapt the weightage between B-mode and enhanced images in dual-screen CEUS, reflecting the clinician's dynamic focus shifts between screens. Secondly, we proposed a Respiratory Motion Compensation (RMC) module to correct motion trajectory interferences due to respiratory motion, effectively leveraging temporal information. We utilized two newly established CEUS datasets, totaling 35,082 frames, to benchmark the ADMNet against various advanced B-mode ultrasound trackers. Our extensive experiments revealed that ADMNet achieves new state-of-the-art performance in CEUS tracking. Ablation studies and visualizations further underline the effectiveness of MAAF and RMC modules, demonstrating the promising potential of ADMNet in clinical CEUS tracing, thus providing novel research avenues in this field.