个人简历

南京航空航天大学计算机科学与技术学院副教授，2018年博士毕业于南京航空航天大学师从张道强教授，2019年至2021年在美国印第安纳大学医学院从事博士后研究，师从Kun Huang教授。主要研究方向为机器学习以及医学图像处理，目前以第一或共同第一作者发表论文10余篇，相关工作发表在Nature Communication, IEEE TMI, MedIA, Bioinformatics, IEEE TCBB等国际一流期刊。 荣获医学图像处理国际顶级会议MICCAI 2019 青年科学家奖，入选2020年度南京航空航天大学长空之星。
教育经历
2014.92018.12南京航空航天大学软件工程工学博士学位
2019.12021.1美国印第安纳大学博士后
2012.112014.6南京安讯科技有限责任公司
2012.62012.11南京烽火星空通信发展有限公司
工作经历
2019.12021.1美国印第安纳大学博士后
2012.112014.6南京安讯科技有限责任公司
2012.62012.11南京烽火星空通信发展有限公司
科研项目
面向癌症预后预测的基因影像学分析方法研究
基于病理图像的癌症预后预测研究

研究领域

机器学习
多组学数据融合
细胞影像学
影像遗传学""

近期论文

1）Shao, W., Han, Z., Cheng, J., Cheng, L., Wang, T., Sun, L., Lu, Z., Zhang, J., Zhang, D. and Huang, K*.,. Integrative analysis of pathological images and multi-dimensional genomic data for early-stage cancer prognosis. IEEE Transactions on Medical Imaging, 39(1), 99-110,2020.
2）Shao, W., Wang, T., Huang, Z., Han, Z., Zhang, J. and Huang, K., Weakly supervised deep ordinal cox model for survival prediction from whole-slide pathological images. IEEE Transactions on Medical Imaging, 40(12), 3739-3747,2021
3) Shao, W., Wang, T., Sun, L., Dong, T., Han, Z., Huang, Z., Zhang, J., Zhang, D. and Huang, K. Multi-task multi-modal learning for joint diagnosis and prognosis of human cancers. Medical Image Analysis, 65:101795, 2020
4）Wang, T*., Shao, W*., Huang, Z., Tang, H., Zhang, J., Ding, Z. and Huang, K. MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification. Nature Communications, 12(1), pp.1-13，2021 (共同第一作者)
5）Shao, W., Huang, S.J., Liu, M. and Zhang, D. Querying Representative and Informative Super-pixels for Filament Segmentation in Bioimages. IEEE Transactions on Computational Biology and Bioinformatics, 17(4), 1394-1405, 2019
6) Shao, W., Liu, M. and Zhang, D*., Human cell structure-driven model construction for predicting protein subcellular location from biological images. Bioinformatics, 32(1), pp.114-121, 2016
7) Shao, W., Liu, M., Xu, Y.Y., Shen, H.B. and Zhang, D.*. An organelle correlation-guided feature selection approach for classifying multi-label subcellular bio-images. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 15(3), pp.828-838, 2017
部分会议论文：
1）Shao, W., Wang, T., Huang, Z., Cheng, J., Han, Z., Zhang, D. and Huang, K*. Diagnosis-Guided Multi-modal Feature Selection for Prognosis Prediction of Lung Squamous Cell Carcinoma. In International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2019), 113-121. 2019.（荣获医学图像分析国际顶级会议MICCAI 2019青年科学家奖，国内唯一，全球共5人）
2) Shao, W., Cheng, J., Sun, L., Han, Z., Feng, Q., Zhang, D. and Huang, K*., Ordinal multi-modal feature selection for survival analysis of early-stage renal cancer. In International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2018), 648-656, 2018.
3) Liu, Z, Shao W, Zhang J, Zhang M, and Huang K. Transfer Learning via Optimal Transportation for Integrative Cancer Patient Stratification. In International Joint Conference on Artificial Intelligence (IJCAI 2021), 221-227, 2021
[1]邵伟,,.Integrative Analysis of Pathological Images and Multi-Dimensional Genomic Data for Early-Stage Cancer Prognosis.IEEE Transaction on Medical Imaging,2020