中 文
Jun Shi, Ph.D, Professor

Office：
825 Xiangying Building, 333 Nanchen Road, Shanghai University, Shanghai

Mail Address(Zip Code)：
Box 83, 99 Shangda Road, Shanghai University, Shanghai (200444)

Phone：
86-

Email：
junshi@shu.edu.cn

URL：
https://scie.shu.edu.cn/Prof/shijun.htm

Research Interests:
Medical Image (Ultrasound, MRI) Analysis, Biomedical Signal (EEG, EEG) Processing, Machine Learning, Rehabilitation Engineering

EducationalBackground:
09/1996~06/2005, Department of Electronic Engineering and Information Science, University of Science and Technology of China (USTC)

WorkingExperiences:
05/2005 – , School of Communication and Information Engineering, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Lecturer, Associate Professor, Professor
01/2011 – 01/2012, BRIC, University of North Carolina at Chapel Hill, Visiting Scholar, Hosted by Professor Dinggang Shen
07/2009 – 10/2009, Hongkong Polytechnic University, Visiting Scholar, Hosted by Professor Yongping Zheng
07/2004 – 11/2004, Hongkong Polytechnic University, Research Assistant
07/2002 – 04/2003, Hongkong Polytechnic University, Research Assistant

Grants and Funding:
National Natural Science Foundation of China (NSFC), Special Fund for Basic Research on Scientific Instruments of NSFC (Co-PI), Key Program of NSFC(Co-PI), Shanghai Municipal Natural Science Foundation, Innovation Program of Shanghai Municipal Education Commission, etc.

Selected Journal Publications:
[1] Jun Shi, Xiao Zheng, Yan Li, Qi Zhang, Shihui Ying. Multimodal neuroimaging feature learning with multimodal stacked deep polynomial networks for diagnosis of Alzheimer's disease. IEEE Journal of Biomedical and Health Informatics. 2018, 22(1): 173-183. (Highly Cited Paper)
[2] Xing Wu*, Cheng Chen, Mingyu Zhong, Jianjia Wang, Jun Shi*. COVID-AL: the diagnosis of COVID-19 with deep active learning. Medical Image Analysis. Accepted.
[3] Feng Shi^#, Jun Wang^#, Jun Shi^#, Ziyan Wu, Qian Wang, Zhenyu Tang, Kelei He, Yinghuan Shi, Dinggang Shen. Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for COVID-19. IEEE Reviews in Biomedical Engineering. 2020. (^#Equal Contribution)
[4] Weiwen Wu, Jun Shi, Hengyong Yu, Weifei Wu* and Varut Vardhanabhuti*. Tensor gradient L0-norm minimization based low-dose CT and its application to COVID-19. IEEE Transactions on Instrumentation & Measurement. Accepted.
[5] Jun Wang, Lichi Zhang, Qian Wang*, Lei Chen, Jun Shi, Xiaobo Chen, Zuoyong Li, and Dinggang Shen*. Multi-class ASD classification based on functional connectivity and functional correlation tensor via multi-source domain adaptation and multi-view sparse representation. IEEE Transactions on Medical Imaging. Accepted.
[6] Xiaoyan Fei, Jun Wang, Shihui Ying, Zhongyi Hu, Jun Shi*. Projective parameter transfer based sparse multiple empirical kernel learning machine for diagnosis of brain disease. Neurocomputing. 2020, 413: 271-283.
[7] Xiaoyan Fei, Lu Shen, Shihui Ying, Yehua Cai, Qi Zhang, Wentao Kong, Weijun Zhou, Jun Shi*. Parameter transfer deep neural network for single-modal B-mode ultrasound-based computer aided diagnosis. Cognitive Computation. 2020, 12: 1252-1264.
[8] Lu Shen, Jun Shi*, Yun Dong, Shihui Ying, Yaxin Peng, Lu Chen, Qi Zhang, Hedi An, Yingchun Zhang. An improved deep polynomial network algorithm for transcranial sonography based diagnosis of Parkinson’s disease. Cognitive Computation. 2020, 12: 553-562.
[9] Jun Shi, Zeyu Xue, Yakang Dai, Bo Peng, Yun Dong, Qi Zhang, Yingchun Zhang. Cascaded multi-column RVFL+ classifier for single-modal neuroimaging-based diagnosis of Parkinson’s disease. IEEE Transactions on Biomedical Engineering. 2019, 66(8): 2362-2371.
[10] Jun Shi, Xiao Zheng, Jinjie Wu, Yan Li, Qi Zhang, Shihui Ying. Quaternion Grassmann average network for learning representation of histopathological image. Pattern Recognition. 2019, 89: 67-76.
[11] Jun Shi, Zheng Li, Shihui Ying, Chaofeng Wang, Qi Zhang, Pingkun Yan. MR image super-resolution via wide residual networks with fixed skip connection. IEEE Journal of Biomedical and Health Informatics. 2019, 23(3): 1129-1140.
[12] Yan Li, Fanqing Meng, Jun Shi*. Learning using privileged information improves neuroimaging-based CAD of Alzheimer's disease: a comparative study. Medical & Biological Engineering & Computing. 2019, 57(7): 1605-1616.
[13] Xiaoyan Fei, Yun Dong, Hedi An, Qi Zhang, Yingchun Zhang, Jun Shi*. Impact of region of interest size on transcranial sonography based computer-aided diagnosis for Parkinson’s disease. Mathematical Biosciences and Engineering. 2019, 16(5): 5640-5651.
[14] Qi Zhang, Shuang Song, Yang Xiao, Shuai Chen, Jun Shi, Hairong Zheng. Dual-modal artificially intelligent diagnosis of breast tumors on both shear-wave elastography and B-mode ultrasound using deep polynomial networks. Medical Engineering and Physics, 2019, 64: 1-6.
[15] Bangming Gong, Jun Shi*, Shihui Ying, Yakang Dai, Qi Zhang, Yun Dong, Hedi An, Yingchun Zhang. Neuroimaging-based diagnosis of Parkinson’s disease with deep neural mapping large margin distribution machine. Neurocomputing. 2018, 320: 141-149.
[16] Jun Shi, Qingping Liu, Chaofeng Wang, Qi Zhang, Shihui Ying, Haoyu Xu. Super-resolution reconstruction of MR image with a novel residual learning network algorithm. Physics in Medicine & Biology. 2018, 63(8):085011.
[17] Lehang Guo, Dan Wang, Yiyi Qian, Xiao Zheng, Chongke Zhao, Xiaolong Li, Xiaowan Bo, Wenwen Yue, Qi Zhang, Jun Shi*, Huixiong Xu. A two-stage multi-view learning framework based computer-aided diagnosis of liver tumors with contrast enhanced ultrasound images. Clinical Hemorheology and Microcirculation. 2018, 69(3): 343-354.
[18] Shihui Ying, Zhijie Wen, Jun Shi, Yaxin Peng, Jigen Peng, Hong Qiao. Manifold preserving: an intrinsic approach for semi-supervised distance metric learning. IEEE Transactions on Neural Networks and Learning Systems. 2018, 29(7): 2731-2742.
[19] Qi Zhang, Yue Liu, Hong Han, Jun Shi, Wenping Wang. Artificial intelligence based diagnosis for cervical lymph node malignancy using the point-wise gated Boltzmann machine. IEEE Access. 2018, 6: 60605 - 60612.
[20] Meihui Qiu, Huifeng Zhang, David Mellor, Jun Shi, Chuangxin Wu, Yueqi Huang, Jianye Zhang, Ting Shen, Daihui Peng. Aberrant neural activity in patients with bipolar depressive disorder distinguishing to the unipolar depressive disorder: a resting-state functional magnetic resonance imaging study. Frontiers in Psychiatry. 2018, 9: 238.
[21] Jun Shi, Jinjie Wu, Yan Li, Qi Zhang, Shihui Ying. Histopathological image classification with color pattern random binary hashing based PCANet and matrix-form classifier. IEEE Journal of Biomedical and Health Informatics. 2017, 21(5): 1327-1337.
[22] Junjie Zhang, Jie Yin, Qi Zhang, Jun Shi*, Yan Li. Robust sound event classification with bilinear multi-column ELM-AE and two-stage ensemble learning. EURASIP Journal on Audio, Speech, and Music Processing. 2017, 11.
[23] Huaipeng Dong, Qi Zhang, Jun Shi. Intensity inhomogeneity compensation and tissue segmentation for magnetic resonance imaging with noise-suppressed multiplicative intrinsic component optimization. Optical Engineering. 2017, 56(12): 123103.
[24] Qi Zhang, Jing Yao, Yehua Cai, Limin Zhang, Yishuo Wu, Jingyu Xiong, Jun Shi, Yuanyuan Wang, Yi Wang. Elevated hardness of peripheral gland on real-time elastography is an independent marker for high-risk prostate cancers. La Radiologia Medica. 2017, 122(12): 944-951.
[25] Qi Zhang,Yang Xiao,Jingfeng Suo, Jun Shi, Jinhua Yu,Yi Guo,Yuanyuan Wang,Hairong Zheng. Sonoelastomics for breast tumor classification: a radiomics approach with clustering-based feature selection on sonoelastography. Ultrasound in Medicine and Biology. 2017, 43(5): 1058-1069.
[26] Qi Zhang, Jingfeng Suo, Wanying Chang, Jun Shi, Man Chen. Dual-modal computer-assisted evaluation of axillary lymph node metastasis in breast cancer patients on both real-time elastography and B-mode ultrasound. European Journal of Radiology, 2017, 95, 66-74.
[27] Qi Zhang, Congcong Yuan, Wei Dai, Lei Tang, Jun Shi, Zuoyong Li, Man Chen. Evaluating pathologic response of breast cancer to neoadjuvant chemotherapy with computer-extracted features from contrast-enhanced ultrasound videos. Physica Medica, 2017, 39, 156-163.
[28] Qi Zhang, Yehua Cai, Yinghui Hua, Jun Shi, Yuanyuan Wang, Yi Wang. Sonoelastography shows that Achilles tendons with insertional tendinopathy are harder than asymptomatic tendons. Knee Surgery, Sports Traumatology, Arthroscopy. 2017, 25: 1839-1848.
[29] Jun Shi, Shichong Zhou, Xiao Liu, Qi Zhang, Minhua Lu, Tianfu Wang. Stacked deep polynomial network based representation learning for tumor classification with small ultrasound image dataset. Neurocomputing. 2016, 194: 87-94.
[30] Qi Zhang, Yang Xiao, Wei Dai, Jingfeng Suo, Congzhi Wang, Jun Shi, Hairong Zheng. Deep learning based classification of breast tumors with shear-wave elastography. Ultrasonics. 2016, 72: 150-157.
[31] Jun Shi, Xiao Liu, Yan Li, Qi Zhang, Yingjie Li, Shihui Ying. Multi-channel EEG based sleep stage classification with joint collaborative representation and multiple kernel learning. Journal of Neuroscience Methods. 2015, 254: 94-101.
[32] Jun Shi, Qikun Jiang, Qi Zhang, Qinghua Huang, Xuelong Li. Sparse kernel entropy component analysis for dimensionality reduction of biomedical data. Neurocomputing. 2015, 168: 930-940.
[33] Jun Shi, Qikun Jiang, Rui Mao, Minhua Lu, Tianfu Wang. FR-KECA: fuzzy robust kernel entropy component analysis. Neurocomputing. 2015, 149: 1415-1423.
[34] Jun Shi, Yi Li, Jie Zhu, Haojie Sun, Yin Cai. Joint sparse coding based spatial pyramid matching for classification of color medical image. Computerized Medical Imaging and Graphics. 2015, 41: 61-66.
[35] Qi Zhang, Chaolun Li, Hong Han, Wei Dai,Jun Shi, YuanyuanWang, WenpingWang. Spatiotemporal quantification of carotid plaque neovascularization on contrast-enhanced ultrasound: correlation with visual grading and histopathology. European Journal of Vascular and Endovascular Surgery. 2015, 50(3): 289-296.
[36] Qi Zhang, Chaolun Li, Moli Zhou, Yu Liao, Chunchun Huang, Jun Shi, Yuanyuan Wang, Wenping Wang. Quantification of carotid plaque elasticity and intraplaque neovascularization using contrast-enhanced ultrasound and imager egistration-based elastography. Ultrasonics. 2015, 62: 253-262.
[37] Huali Chang, Zhenping Chen, Qinghua Huang, Jun Shi, Xuelong Li. Graph-based learning for segmentation of 3D ultrasound images. Neurocomputing. 2015, 151: 632-644.
[38] Jun Shi, Yin Cai, Jie Zhu, Jin Zhong, Fei Wang. SEMG-based hand motion recognition using cumulative residual entropy and extreme learning machine. Medical & Biological Engineering & Computing. 2013, 51(4): 417-427.
[39] Shichong Zhou, Jun Shi*, Jie Zhu, Yin Cai, Ruiling Wang. Shearlet-based texture feature extraction for classification of breast tumor in ultrasound image. Biomedical Signal Processing and Control. 2013, 8(6): 688-696.
[40] Jun Shi, Jingyi Guo, Shuxian Hu, Yongping Zheng. Recognition of finger flexion motion from ultrasound image: a feasibility study. Ultrasound in Medicine and Biology. 2012, 38(10): 1695-1704.
[41] Jun Shi, Qian Chang, Yongping Zheng. Feasibility of controlling a prosthetic hand using sonomyography signal in real time: a preliminary study. Journal of Rehabilitation Research and Development. 2010, 47(2): 87-98.
[42] Jiehui Jiang, Zhuangzhi Yan, Jun Shi, et al. A mobile monitoring system of blood pressure for underserved in China by information and communication technology service. IEEE Transactions on Information Technology in Biomedicine. 2010, 14(3): 748-757.
[43] Xin Chen, Yongping Zheng, Jingyi Guo, Jun Shi. Sonomyography (SMG) Control for Powered Prosthetic Hand: A Study with Normal Subjects. Ultrasound in Medicine and Biology. 2010, 36(7): 1076-1088.
[44] Jun Shi, Yongping Zheng, Xin Chen, Hongbo Xie. Modeling the relationship between wrist angle and muscle thickness during wrist flexion-extension based on the bone-muscle lever system: a comparison study. Medical Engineering and Physics. 2009, 31(10): 1125-1160.
[45] Hongbo Xie, Yongping Zheng, Jingyi Guo, Xin Chen, Jun Shi. Estimation of wrist angle from sonomyography using support vector machine and artificial neural network models. Medical Engineering and Physics. 2009, 31(3): 384-391.
[46] Jun Shi, Yongping Zheng, Qinghua Huang, Xin Chen. Continuous monitoring of sonomyography, electromyography and torque generated by normal upper arm muscles during isometric contraction: sonomyography assessment for arm muscles. IEEE Transactions on Biomedical Engineering. 2008, 55(3): 1191-1198.
[47] Jun Shi, Yongping Zheng, Xin Chen, et al. Assessment of muscle fatigue using sonomyography: muscle thickness change detected from ultrasound images. Medical Engineering and Physics. 2007, 29(4): 472-479.
[48] Yongping Zheng, Matthew Chan, Jun Shi, et al. Sonomyography: monitoring morphological changes of forearm muscles in actions with the feasibility for the control of powered prosthesis. Medical Engineering and Physics. 2006, 28: 405-415.
[49] Yongping Zheng, Jun Shi, et al. Dynamic Depth-dependent Osmotic Swelling and Solute Diffusion in Articular Cartilage Monitored using Real-time Ultrasound. Ultrasound in Medicine and Biology. 2004, 30 (6): 841-849.
[50] Yongping Zheng, SL Bridal, Jun Shi, et al. High resolution ultrasound elastomicroscopy imaging of soft tissues: System development and feasibility. Physics in Medicine and Biology. 2004, 49(17): 3925-3938.

Selected Conference Publications:
[1] Zhiyang Lu, Jun Li, Zheng Li, Hongjian He, Jun Shi*. Reconstruction of quantitative susceptibility maps from phase of susceptibility weighted imaging with cross-connected Ψ-Net. The 2021 IEEE International Symposium on Biomedical Imaging (ISBI). Accepted.
[2] Xiangmin Han, Jun Wang, Weijun Zhou, Cai Chang, Shihui Ying, Jun Shi*. Deep doubly supervised transfer network for diagnosis of breast cancer with imbalanced ultrasound imaging modalities. The 23nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). 2020.
[3] Bangming Gong, Lu Shen, Cai Chang, Shichong Zhou, Weijun Zhou, Shuo Li, Jun Shi*. Bi-modal ultrasound breast cancer diagnosis via multi-view deep neural network SVM. IEEE International Symposium on Biomedical Imaging (ISBI). 2020.
[4] Zheng Li, Qingping Liu, Yiran Li, Qiu Ge, Yuanqi Shang, Donghui Song, Ze Wang*, Jun Shi*. A two-stage multi-loss super-resolution network for arterial spin labeling magnetic resonance imaging. The 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). 2019. (Graduate Student Travel Award)
[5] Xiaoyan Fei, Weijun Zhou, Lu Shen, Cai Chang, Wentao Kong, Shichong Zhou, Jun Shi*, Ultrasound-based diagnosis of breast tumor with parameter transfer multilayer kernel extreme learning machine. The 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS). 2019.
[6] Jun Shi, Minjun Yan, Yun Dong, Xiao Zheng, Qi Zhang, Hedi An. Multiple kernel learning based classification of Parkinson’s disease with multi-modal transcranial sonography. The 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS). 2018. (Oral Representation)
[7] Lu Shen, Jun Shi*, Bangming Gong, Yingchun Zhang, Yun Dong, Qi Zhang, Hedi An. Multiple empirical kernel mapping based broad learning system for classification of Parkinson’s disease with transcranial sonography. The 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS). 2018. (Oral Representation)
[8] Fanqing Meng, Jun Shi*, Bangming Gong, Qi Zhang, Lehang Guo, Dan Wang, Huixiong Xu*. B-mode ultrasound based diagnosis of liver cancer with CEUS images as privileged information. The 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS). 2018. (Oral Representation)
[9] Zeyu Xue, Jun Shi*, Yakang Dai, Yun Dong, Qi Zhang, Yingchun Zhang. Transcranial sonography based diagnosis of Parkinson’s disease via cascaded kernel RVFL+. The 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS). 2018.
[10] Haohao Xu, Qi Zhang, Huaipeng Dong, Xiyuan Jiang, Jun Shi. Suppression of ultrasonography using maximum likelihood estimation and weighted nuclear norm minimization. The 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS). 2018.
[11] Qingping Liu, Jun Shi*, Ze Wang. Reconstructing high-resolution arterial spin labeling perfusion images via convolutional neural networks residual-learning based methods. Joint Annual Meeting ISMRM-ESMRMB. 2018.
[12] Xiao Zheng, Jun Shi*, Qi Zhang, Shihui Ying, Yan Li. Improving MRI-based diagnosis of Alzheimer’s disease via an ensemble privileged information learning algorithm. 2017IEEE International Symposium on Biomedical Imaging (ISBI). 2017. (Oral Representation)
[13] Chaofeng Wang, Jun Shi*, Qi Zhang, Shihui Ying. Histopathological image classification with bilinear convolutional neural networks. The 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS). 2017. (Oral Representation)
[14] Yiyi Qian, Jun Shi*, Xiao Zheng, Qi Zhang, Lehang Guo, Dan Wang, and Huixiong Xu. Multimodal ultrasound imaging based diagnosis of liver cancers with a two-stage multi-view learning framework. The 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS). 2017. (Oral Representation)
[15] Lehang Guo, Dan Wang, Huixiong Xu, Yiyi Qian, Chaofeng Wang, Xiao Zheng, Qi Zhang, Jun Shi*. CEUS-based classification of liver tumors with deep canonical correlation analysis and multi-kernel learning. The 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS). 2017. (Oral Representation)
[16] Jinjie Wu, Jun Shi*, Yan Li, Jingfeng Suo, Qi Zhang. Histopathological image classification using random binary hashing based PCANet and bilinear classifier. The 2016 European Signal Processing Conference (EUSIPCO). 2016. (Oral Representation)
[17] Xiao Zheng, Jun Shi*, Yan Li, Xiao Liu, Qi Zhang. Multi-modality stacked deep polynomial network based feature learning for Alzheimer’s disease diagnosis. 2016IEEE International Symposium on Biomedical Imaging (ISBI). 2016.
[18] Xiao Zheng, Jun Shi*, Shihui Ying, Qi Zhang, Yan Li. Improving single-modal neuroimaging based diagnosis of brain disorders via boosted privileged information learning framework. 2016 MICCAI Workshop on Machine Learning in Medical Imaging (MLMI). 2016.
[19] Jinjie Wu, Jun Shi*, Shihui Ying, Qi Zhang, Yan Li. Learning representation for histopathological image with quaternion Grassmann average network. 2016 MICCAI Workshop on Machine Learning in Medical Imaging (MLMI). 2016.
[20] Xiao Liu, Jun Shi*, Qi Zhang. Tumor classification by deep polynomial network and multiple kernel learning on small ultrasound image dataset. 2015 MICCAI Workshop on Machine Learning in Medical Imaging (MLMI). 2015.
[21] Jie Zhu, Jun Shi*. Hessian regularization based semi-supervised dimensionality reduction for neuroimaging data of Alzheimer’s disease. 2014IEEE International Symposium on Biomedical Imaging (ISBI). 2014.
[22] Xiao Liu, Jun Shi*, Shichong Zhou, Minhua Lu. An iterated Laplacian based semi-supervised dimensionality reduction for classification of breast cancer on ultrasound images. The 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS). 2014.
[23] Qikun Jiang, Jun Shi*. Sparse kernel entropy component analysis for dimensionality reduction of neuroimaging data. The 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS). 2014.
[24] Jun Shi, Yin Cai. Joint sparse coding spatial pyramid matching for classification of color blood cell image. 2013 MICCAI Workshop on Machine Learning in Medical Imaging (MLMI). 2013.