研究方向：人工智能与医学图像分析交叉。专注于超声图像分析，及其是超声在在胎儿大脑筛查，甲乳腺癌症诊断等相关应用。

 科研项目

（1）科技部-国家重点研发计划，智能医学超声前沿理论、关键技术及临床应用研究（2019YFC0118300），2019.12.1-2021.12.31，429万元，参与。

 代表性成果论文（*表示通讯作者，——此处提供英文代表作最多五篇，除中医药、医学人文专业外，不需提供中文代表作）

（1）l Jian, W., Juzheng, M., Xin, Y., Rui, L., Guangquan, Z., Yuhao, H., Zehui, L., Wufeng, X., Xiaohong, J., Jianqiao, Z. , Ruobing, H* and Dong Ni., 2020. Auto-weighting for Breast Cancer Classification in Multimodal Ultrasound. arXiv preprint arXiv:2008.03435,(accepted in MICCAI2020).
（2）Huang, R., Xie, W. and Noble, J.A*., 2018. VP-Nets: Efficient automatic localization of key brain structures in 3D fetal neurosonography. Medical image analysis, 47, pp.127-139.
（3）Huang, R., Noble, J.A. and Namburete, A.I*., 2018, September. Omni-supervised learning: scaling up to large unlabelled medical datasets. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 572-580). Springer, Cham.
（4）Huang, R., Namburete, A. and Noble, A*., 2018. Learning to segment key clinical anatomical structures in fetal neurosonography informed by a region-based descriptor. Journal of Medical Imaging, 5(1), p.014007