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Abstract #1674

Multi-modal fusion with joint conditional transformer for grading hepatocellular carcinoma

Shangxuan Li1, Yanshu Fang2, Guangyi Wang3, Lijuan Zhang4, and Wu Zhou1
1School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, China, 2First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangxhou, China, 3Department of Radiology, Guangdong Provincial People’s Hospital, Guangzhou, China, 4Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

Synopsis

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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