Multi-Instance Multi-Label Learning for Gene Mutation Prediction in Hepatocellular Carcinoma

Multi-Instance Multi-Label Learning for Gene Mutation Prediction in Hepatocellular Carcinoma

2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

10.1109/EMBC44109.2020.9175293

https://ieeexplore.ieee.org/document/9175293/authors#authors

Kaixin Xu, Ziyuan Zhao, Jiapan Gu, Zeng Zeng, Chan Wan Ying, Lim Kheng Choon, Thng Choon Hua, Pierce KH Chow

Tumors

01 August 2020

@INPROCEEDINGS{9175293, author={K. {Xu} and Z. {Zhao} and J. {Gu} and Z. {Zeng} and C. W. {Ying} and L. K. {Choon} and T. C. {Hua} and P. K. {Chow}}, booktitle={2020 42nd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC)}, title={Multi-Instance Multi-Label Learning for Gene Mutation Prediction in Hepatocellular Carcinoma}, year={2020}, volume={}, number={}, pages={6095-6098},}

Gene mutation prediction in hepatocellular carcinoma (HCC) is of great diagnostic and prognostic value for personalized treatments and precision medicine. In this paper, we tackle this problem with multi-instance multi-label learning to address the difficulties on label correlations, label representations, etc. Furthermore, an effective oversampling strategy is applied for data imbalance. Experimental results have shown the superiority of the proposed approach.

PublisherCopyrights

The work was supported by Pre-GAP Grant, Singapore (Grant No. ACCL/19-GAP023-R20H) and Singapore-China NRF-NSFC Grant (Grant No. NRF2016NRF-NSFC001-111).

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Institute for Infocomm Research