The combination of context information from multi-modalities is remarkably significant for lesion characterization. However, there are still two remaining challenges for multi-modalities based lesion characterization including features overlapping between different tumor grades and large differences in modal contributions. In this work, we proposed a discriminative feature learning and adaptive fusion method in the framework of deep learning architecture for improving the performance of multimodal fusion based lesion characterization. Experimental results of grading of clinical hepatocellular carcinoma (HCC) demonstrate that the proposed method outperforms the previously reported fusion methods, including concatenation, correlated and individual feature learning, and deeply supervised net.
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