Keywords: Cancer, Machine Learning/Artificial IntelligenceMultimodal medical imaging plays an important role in the diagnosis and characterization of lesions. Transformer pays more attention to global relationship modeling in data, which has obtained promising performance in lesion characterization. We propose a multi-modal fusion network with jointly conditional transformer to realize adaptive fusion of multimodality information and mono-modality feature learning constrained by other modal conditions. The experimental results of the clinical hepatocellular carcinoma (HCC) dataset show that the proposed method is superior to the previously reported multimodal fusion methods for HCC grading.
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