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Multi-Phase Cross-modal Learning for Noninvasive Gene Mutation Prediction in Hepatocellular Carcinoma
- Source :
- 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
- Publication Year :
- 2020
-
Abstract
- Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and the fourth most common cause of cancer-related death worldwide. Understanding the underlying gene mutations in HCC provides great prognostic value for treatment planning and targeted therapy. Radiogenomics has revealed an association between non-invasive imaging features and molecular genomics. However, imaging feature identification is laborious and error-prone. In this paper, we propose an end-to-end deep learning framework for mutation prediction in APOB, COL11A1 and ATRX genes using multiphasic CT scans. Considering intra-tumour heterogeneity (ITH) in HCC, multi-region sampling technology is implemented to generate the dataset for experiments. Experimental results demonstrate the effectiveness of the proposed model.<br />Comment: Accepted version to be published in the 42nd IEEE Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2020, Montreal, Canada
Details
- Database :
- arXiv
- Journal :
- 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
- Publication Type :
- Report
- Accession number :
- edsarx.2005.04069
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1109/EMBC44109.2020.9176677