个人简介：

强宁，副教授/硕士生导师，西北工业大学工学博士，美国佐治亚大学访问学者，从事医学影像人工智能研究，主持/参与国家自然科学基金、陕西省自然科学基金及各类横向项目10余项，在领域内顶刊MIA、IEEE TBME、IEEE TCDS等发表学术论文30余篇，授权国家发明专利8项。教学方面，长期从事各类大学生竞赛的组织和培训工作，指导学生获奖20余项。

教育经历：

美国佐治亚大学CAID实验室，访问学者，2018，导师：刘天明

西北工业大学工学博士，2016，导师：康凤举

研究方向：

基于人工智能的医学影像分析、脑疾病的辅助诊断、基于脑电的脑认知功能探索、通用人工智能技术应用。

讲授课程：

电路分析、单片机原理、PCB电路设计、自动控制原理等。

欢迎对医学影像人工智能感兴趣的同学报考！优秀硕士毕业生可推荐上985博士。

近年来发表论文：

(1) Gao J, Ge B, Ning Qiang, S. Zhao. 3D masked autoencoder with spatiotemporal transformer for modeling of 4D fMRI data[J]. Medical Image Analysis: 103861. (IF=11.8,一区TOP), 2026.

(2) Sun C, Feng R, Liu M, Ma S, Tai J, Hu J, Li J, Ning Qiang. Combining Static and Dynamic Brain Network Analysis with Machine Learning for Enhanced Diagnosis of Major Depressive Disorder[J]. Psychiatry Research: Neuroimaging, (IF=2.1，SCI三区), 2025.

(3) Ning Qiang, Dong Q, Huang H, Han Wang, Shijie Zhao, Xintao Hu, Qing Li, Wei Zhang, Yiheng Liu, Mengshen He, Bao Ge, Lin Zhao, Zihao Wu, Lu Zhang, Steven Xu, Dajiang Zhu, Xi Jiang, Tianming Liu. Deep learning in functional brain mapping and associated applications[M]//Deep Learning for Medical Image Analysis (Second edition) (医学影像国际图书). Academic Press, 2024: 395-423

(4) Yiheng Liu, Enjie Ge, Mengshen He, Zhengliang Liu, Shijie Zhao, Xintao Hu, Ning Qiang, Dajiang Zhu, Tianming Liu and Bao Ge，Mapping dynamic spatial patterns of brain function with spatial wise attention. Journal of Neural Engineering (IF=4，SCI二区), 2024.

(5) Liu, Y., Ge, E., Z. Kang, Ning Qiang, Liu, T., & Ge, B.. Spatial-Temporal Convolutional Attention for Mapping Functional Brain Networks. Neuroimage. (IF=5.7 ，SCI二区), 2024.

(6) Ning Qiang, Gao J, Dong Q, et al. Functional brain network identification and fMRI augmentation using a VAE-GAN framework [J]. Computers in Biology and Medicine, 2023. (IF=7.7 SCI二区).

(7) Ning Qiang, Gao J, Dong Q, et al. A deep learning method for autism spectrum disorder identification based on interactions of hierarchical brain networks[J]. Behavioural Brain Research, 2023: 114603. (IF=2.7 SCI三区).

(8) Zhao, Shijie and Li, Wenyuan and liu, Zhuoyan and Pang, Tianji and Yang, Yang and Qiang, Ning and Zhao, Jingyi and Li, Bangguo and Lei, Baiying and Han, Junwei, End-to-end Prediction of EGFR Mutation Status with Denseformer,  IEEE Journal of Biomedical and Health Informatics,2023. (IF=7.7, SCI一区)

(9) Zhang, S., Wu, L., Yu, S., Shi, E., Ning Qiang, Gao, H., ... & Zhao, S. (2022). An Explainable and Generalizable Recurrent Neural Network Approach for Differentiating Human Brain States on EEG Dataset. IEEE Transactions on Neural Networks and Learning Systems,2023. (IF=14.25, SCI一区).

(10) He, M., Hou, X., Wang, Z., Kang, Z., Zhang, X., N. Qiang, & Ge, B. (2022). Multi-head Attention-Based Masked Sequence Model for Mapping Functional Brain Networks. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 295-304). Springer, Cham. (MICCAI 2022)

(11) Wang, Z., Lv, Y., He, M., Ge, E., Qiang, N. Qiang, & Ge, B. (2022). Accurate Corresponding Fiber Tract Segmentation via FiberGeoMap Learner. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 143-152). Springer, Cham. (MICCAI 2022)

(12) Ning Qiang, Dong, Q., H. Liang, Ge, B., Ge, F., Liang, C. Zhang, Liu. T, J. Gao, H. Yue, S. Zhao. Learning Brain Representation using Recurrent Wasserstein Generative Adversarial Net. Computer methods and programs in biomedicine. (IF=7.027，SCI二区), 2022

(13) Ning Qiang, Dong, Q., H.L., B. Ge, C. Zhang, et al. A novel ADHD classification method based on resting state temporal templates (RSTT) using spatiotemporal attention auto-encoder. Neural computing and applications (IF=5.102, SCI二区), 2022.

(14) Ning Qiang, Dong, Q., Ge, B., Ge, F., Liang, C. Zhang, J. Gao, and Liu, T. Modeling and Augmenting of fMRI Data using Deep Recurrent Variational Auto-encoder. Journal of neural engineering (IF=5.043，SCI二区), 2021.

(15) Ning Qiang, Dong, Q., Ge, B., Ge, F., Liang, C. Zhang, J. Gao, and Liu, T. Deep variational autoencoder for mapping functional brain networks. IEEE transactions on cognitive and developmental systems (IF=4.546, SCI三区), 2021.

(16) Ning Qiang, Dong, Q., Zhang, W., Ge, B., Ge, F., Liang, H., ... & Liu, T. (2020). Modeling Task-based fMRI Data via Deep Belief Network with Neural Architecture Search. Computerized Medical Imaging and Graphics (IF=7.422, SCI二区), vol.83, 2020, 101747.

(17) Ning Qiang, Ge, B., Dong, Q., Ge, F., & Liu, T. (2019, October). Neural Architecture Search for Optimizing Deep Belief Network Models of fMRI Data.  International Workshop on Multiscale Multimodal Medical Imaging (pp. 26-34). Springer, Cham. (Workshop of MICCAI2019), Oral presentation.

(18) Ning Qiang, Ge, B., Dong, Q., Ge, F., & Liu, T. (2020, July). Deep Variational Autoencoder for Modeling Functional Brain Networks and ADHD Identification. 2020 IEEE 17th International Symposium on Biomedical Imaging. Springer, Cham. (ISBI2020), Oral presentation.

(19) Q. Dong*, Ning Qiang*, et al. Spatiotemporal Attention Autoencoder (STAAE) for ADHD Classification [C]. Medical image computing and computer-assisted intervention (MICCAI2020), 2020. (* joint first author).

(20) Qinglin Dong, Fangfei Ge, Ning Qiang, Tianming Liu. Modeling Hierarchical Brain Networks via Volumetric Sparse Deep Belief Network (VS-DBN). IEEE Transactions on Biomedical Engineering, 2019

(21) Q. Dong*, Ning Qiang*, et al. Discovering Functional Brain Networks with 3D Residual Autoencoder (ResAE).[C]. Medical image computing and computer-assisted intervention (MICCAI2020), 2020. (* joint first author).

(22) Qinglin Dong*, Ning Qiang* , Jinglei Lv, Xiang Li, Tianming Liu, Quanzheng Li, A Novel fMRI Representation Learning Framework with GAN. pp. 498-507. Medical image computing and computer-assisted intervention (Workshop of MICCAI2020), 2020. (* joint first author).

(23) Qing Li, Qinglin Dong, Fangfei Ge，Ning Qiang, Xia Wu, Tianming Liu. Simultaneous Spatial-temporal Decomposition of Connectome-scale Brain Networks by Deep Sparse Recurrent Auto-encoder. 26th International Conference, IPMI 2019, Hong Kong, China, June 2–7, 2019, Proceedings, Lecture Notes in Computer Science, vol 11492. Springer, 579-591