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Abstract #0536

Discriminative feature learning and adaptive fusion for the grading of hepatocelluar carcinoma with Contrast-enhanced MR

Wu Zhou1, Shangxuan Li1, Wanwei Jian1, Guangyi Wang2, Lijuan Zhang3, and Honglai Zhang1
1School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, China, 2Department of Radiology, Guangdong General Hospital, Guangzhou, China, 3Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

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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