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Abstract #4331

Correlated and specific features fusion based on attention mechanism for grading hepatocellular carcinoma with Contrast-enhanced MR

Shangxuan Li1, Guangyi Wang2, Lijuan Zhang3, and Wu Zhou1
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


Contrast-enhanced MR plays an important role in the characterization of hepatocellular carcinoma (HCC). In this work, we propose an attention-based common and specific features fusion network (ACSF-net) for grading HCC with Contrast-enhanced MR. Specifically, we introduce the correlated and individual components analysis to extract the common and specific features of Contrast-enhanced MR. Moreover, we propose an attention-based fusion module to adaptively fuse the common and specific features for better grading. Experimental results demonstrate that the proposed ACSF-net outperforms previously reported multimodality fusion methods for grading HCC. In addition, the weighting coefficient may have great potential for clinical interpretation.

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