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强宁
职称/职务: 副教授
电话:
传真:
电子信箱: qn315@snnu.edu.cn
出生年月:
办公地点: 致知3617

个人简介:

强宁,博士,副教授,毕业于西北工业大学,MICCAIMICS会员。目前感兴趣的研究方向为基于fMRIEEG的功能脑网络分析、脑疾病的辅助诊断,包括注意力缺陷多动症(ADHD)、自闭症(Autism)、阿尔兹海默症(ADIN)等,以及深度学习在生物医学、教育学等领域的交叉应用研究,已发表学术论文三十余篇,主持和参与多项科研项目。教学方面,长期从事大学生电子设计竞赛的组织和培训工作,培养学生获奖十余项,并指导多项大学生创新创业项目。

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:

基于深度学习的脑影像分析、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。

近年来发表论文:

(1) 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,小类一区).

(2) 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).

(3) 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)

(4) 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).

(5) 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)

(6) 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)

(7) 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=7.027SCI二区), 2022

(8) 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.

(9) 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=5.043SCI二区), 2021.

(10) 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.

(11) 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.

(12) 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.

(13) 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.

(14) 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).

(15) 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

(16) 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).

(17) 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).

(18) Qing Li, Qinglin Dong, Fangfei GeNing 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

 

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