本人目前为南京航空航天大学计算机科学与技术学院/人工智能学院副教授，博士生导师，主要研究方向为机器学习、医学图像分析等。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。目前主要从事人工智能与医学交叉研究，目前主持国家自然科学基金面上项目等纵向课题5项。共发表CNS 子刊/IEEE 会刊/CCF-A 会议 34 篇，谷歌学术引用 2100余次。近 5 年以第一（共同第一）、通讯（共同通讯）作者发表在 Nature Communications（2 篇）、Cell Reports (2 篇)、IEEE Transactions on Medical Imaging (8 篇)、 Nano Today(1 篇) 等国际权威学术期刊(总 IF 210.4, 其中 14篇 IF>10)以及 CVPR、MICCA、IPMI 等国际重要会议上，相关成果在Nature Medicine等权威期刊受到美、英、 加等多国院士好评，入选斯坦福大学2024年全球前2%顶尖科学家榜单。 研究成果2次获医学影像分析权威国际会议MICCAI青年科学家奖（大陆唯一，全球5人），全国博士后创新创业大赛银奖，第三届江苏大数据开发与应用大赛医疗卫生赛道一等奖等荣誉。申请人目前担任医学影像计算青年研讨会(MICS)委员、中国计算机学会生物信息专委会通讯委员、中国人工智能学会(CAAI)机器学习专委会通讯委员等学术任职。受邀在第十届医学影像计算青年研讨会、2022 年 IEEE 数字孪生和平行智能会议、2023年国际阿尔茨海默病及相关病学术大会等会议做报告，并担任国际期刊 Diagnostic, Artificial Intelligence in Health编委以及国内期刊《数据采集》青年编委。 此外，申请人还受邀在医学影像分析权威国际学术会议 MICCAI2024 上联合举办“智能影像基因组学”专题研讨会。申请人基于已有学术成果搭建智能软件平台3套，包括：全栈式病理影像分析平台、宫颈细胞病理筛查系统、空间多组学数据分析平台并在多家三甲医院得到应用。

招生要求：

1.  热爱科研，对人工智能方向具有浓厚的兴趣。

2.  有一定的编码基础，了解一种或多种深度学习编程框架(Pytorch, Tensorflow等)。

3.  具有一定的英文阅读与写作能力。

部分期刊论文：

  1) Guan, X., Zhu, Q., Sun, L., Zhao, J., Zhang, D., Wan, P., Shao,W*. (2024). Global-local consistent semi-supervised segmentation of histopathological image with different perturbations. Pattern Recognition(机器学习顶级期刊，中科院1区), 110696.(通讯作者)

  2) Shao, W., Shi, H., Liu, J., Zuo, Y., Sun, L., Xia, T., Chen, W., Wan, P., Sheng, J., Zhu, Q. and Zhang, D., 2024. Multi-instance Multi-task Learning for Joint Clinical Outcome and Genomic Profile Predictions from the Histopathological Images. IEEE Transactions on Medical Imaging（医学图像分析顶级期刊，中科院1区）,in press.

  3) Du, J., Zhang, J., Wang, L., Wang, X., Zhao, Y., Lu, J., Fan, T., Niu, M., Zhang, J., Cheng, F. Li, J., Shao, W*  and  Sheng, J. Selective oxidative protection leads to tissue topological changes orchestrated by macrophage during ulcerative colitis. Nature Communications（Nature子刊，中科院1区）, 14(1), p.3675, 2023. (通讯作者)

  4) Zhang, J., Song, J., Tang, S., Zhao, Y., Wang, L., Luo, Y., Tang, J., Ji, Y., Wang, X., Li, T.， Zhang, H.,  Shao, W*.,  Sheng, J., Liang, T and Bai, X. Multi-omics analysis reveals the chemoresistance mechanism of proliferating tissue-resident macrophages in PDAC via metabolic adaptation. Cell Reports（Cell子刊，中科院1区）, 42(6),  2023. (通讯作者)

  5) Zhao X ., Song Y., Bao  X ., Zhang L., Wu J., Wang L., He C ., Shao W*., Bai X , Liang T and  Sheng J.  Single-cell RNA sequencing-guided fate-mapping toolkit delineates the contribution of yolk sac erythro-myeloid progenitors. Cell Reports（Cell子刊，中科院1区）, 42(11), p.2211, 2023(通讯作者)   

  6) Shao W, Zuo Y, Shi Y, Wu Y, Tang J, Zhao J, Sun L, Lu Z, Sheng J, Zhu Q, Zhang D. Characterizing the Survival-Associated Interactions between Tumor-infiltrating Lymphocytes and Tumors from Pathological Images and Multi-omics Data. IEEE Transactions on Medical Imaging（医学图像分析顶级期刊，中科院1区）. 2023 May 9.

  7) Shao W,  Liu J, Zuo Y, Qi S, Hong H, Sheng J, Zhu Q, Zhang D. FAM3L: Feature-Aware Multi-modal Metric Learning for Integrative Survival Analysis of Human Cancers. IEEE Transactions on Medical Imaging（医学图像分析顶级期刊，中科院1区）. 2023 Mar 27.

  8）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（医学图像分析顶级期刊，中科院1区）, 39(1), 99-110,2020. 

  9）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（医学图像分析顶级期刊，中科院1区）, 40(12), 3739-3747,2021   

  10) 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（医学图像分析顶级期刊，中科院1区）, 65:101795, 2020

  11）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（Nature子刊，中科院1区）, 12(1), pp.1-13，2021 (共同第一作者) 

  12) Huang, Z., Shao, W*., Han, Z., Alkashash, A.M., De la Sancha, C., Parwani, A.V., Nitta, H., Hou, Y., Wang, T., Salama, P. and Rizkalla, M., 2023. Artificial intelligence reveals features associated with breast cancer neoadjuvant chemotherapy responses from multi-stain histopathologic images. NPJ Precision Oncology（Nature子刊，中科院1区）, 7(1), pp.14-21(共同第一作者)

  13) Zhu, Q., Xu, B., Huang, J., Wang, H., Xu, R., Shao,W* and Zhang,D. Deep Multi-Modal Discriminative and Interpretability Network for Alzheimer’s Disease Diagnosis. IEEE Transactions on Medical Imaging（医学图像分析顶级期刊，中科院1区）. in press, pp.1-13, 2022(通讯作者)

  14）Zhu, Q., Wang, H., Xu, B., Zhang, Z., Shao, W* and Zhang, D. Multimodal Triplet Attention Network for Brain Disease Diagnosis. IEEE Transactions on Medical Imaging（医学图像分析顶级期刊，中科院1区）, 41(12), 2022，3884-3894（通讯作者）.

  15）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（生物信息学顶级期刊，中科院2区）, 17(4),  1394-1405,   2019 

    16) Shao, W., Liu, M. and Zhang, D*., Human cell structure-driven model construction for predicting protein subcellular location from biological images. Bioinformatics（生物信息学顶级期刊，中科院2区）, 32(1), pp.114-121, 2016

  17) 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（生物信息学顶级期刊，中科院2区）, 15(3), pp.828-838, 2017

 

部分会议论文：

1)Tang,J.,Y,Gao.,P Wan.,M Wang.,D Zhang.,and W Shao*. OSAL-ND：Open-set Active Learning for Nucleus Detection.In International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2024), early-accept(通讯作者)

2）Shao,W., Y,Shi, Zhang, D, Zhou, J and Wan P. Tumor Micro-environment Interactions Guided Graph Learning for Survival Analysis of Human Cancers from Whole-slide Pathological Images.In Proceedings of Computer Vision and Pattern Recognition (CVPR 2024)

3）Zuo, Y., Wu, Y., Lu, Z., Zhu, Q., Huang, K., Zhang, D. and Shao, W*. Identify Consistent Imaging Genomic Biomarkers for Characterizing the Survival-Associated Interactions Between Tumor-Infiltrating Lymphocytes and Tumors. In International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2022),222-231, 2022(指导学生获医学图像分析国际顶级会议MICCAI 2022青年科学家奖，全球共5人，通讯作者)

4）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）

5) 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. 

6) 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