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Early diagnosis of renal cancer can greatly improve the survival rate ofpatients. Contrast-enhanced ultrasound (CEUS) is a cost-effective andnon-invasive imaging technique and has become more and more frequently used forrenal tumor diagnosis. However, the classification of benign and malignantrenal tumors can still be very challenging due to the highly heterogeneousappearance of cancer and imaging artifacts. Our aim is to detect and classifyrenal tumors by integrating B-mode and CEUS-mode ultrasound videos. To thisend, we propose a novel multi-modal ultrasound video fusion network that caneffectively perform multi-modal feature fusion and video classification forrenal tumor diagnosis. The attention-based multi-modal fusion module usescross-attention and self-attention to extract modality-invariant features andmodality-specific features in parallel. In addition, we design an object-leveltemporal aggregation (OTA) module that can automatically filter low-qualityfeatures and efficiently integrate temporal information from multiple frames toimprove the accuracy of tumor diagnosis. Experimental results on a multicenterdataset show that the proposed framework outperforms the single-modal modelsand the competing methods. Furthermore, our OTA module achieves higherclassification accuracy than the frame-level predictions. Our code is availableat \url{https://github.com/JeunyuLi/MUAF}.

MUVF-YOLOX: A Multi-modal Ultrasound Video Fusion Network for Renal Tumor Diagnosis