English

施俊 博士，教授，博导，副院长

办公室：

上海大学南陈路333号翔英大楼529室

通信地址(邮政编码):

上海市上大路99号83信箱（200444）

电话：

021 - 66138178 / 66137269

电子邮件：

junshi@shu.edu.cn

个人主页：

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

研究方向：

       机器学习（深度学习）方法、医学图像（超声图像、核磁共振成像等）分析、医学信号（脑电信号、肌电信号等）处理、康复工程

教育经历:

2000.09 – 2005.06，中国科学技术大学，电子工程与信息科学系，博士

1996.09 – 2000.06，中国科学技术大学，电子工程与信息科学系，学士

工作经历：

2005.06 – 至今，上海大学，通信与信息工程学院，讲师，副教授，教授

2011.01 – 2012.01，北卡罗来纳大学教堂山分校，生物医学成像中心，访问学者

2009.07 – 2009.10，香港理工大学，医疗科技与资讯学系，访问学者

2004.07 – 2004.11，香港理工大学，赛马会复康科技中心，研究助理

2002.07 – 2003.04，香港理工大学，赛马会复康科技中心，研究助理

学术活动：

MICS委员会轮值主席（2020-2021）

会议主席：2016医学影像信息处理研讨会暨第二届长三角地区医学影像分析论坛

组委会成员：2017第四届医学图像计算青年研讨会

主持项目：

国家自然科学基金面上项目、国家自然科学基金青年基金项目、国家自然科学基金重大科研仪器研制项目（合作单位主持）、国家自然科学基金重点项目（合作单位主持）、上海市自然科学基金面上项目、上海市科委项目、上海市教委项目等。

代表性期刊论文：

[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] 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. 2021, 14: 4-15. (#Equal Contribution, Highly Cited Paper)

[3] Zhongyi Hu, Jun Wang, Chunxiang Zhang, Zhenzhen Luo, Xiaoqing Luo, Lei Xiao, Jun Shi. Uncertainty modeling for multi-center Autism Spectrum Disorder classification using Takagi-Sugeno-Kang fuzzy systems. IEEE Transactions on Cognitive and Developmental Systems. 2022, 14(2): 730-739. (Highly Cited Paper)

[4] 施俊, 汪琳琳, 王珊珊, 陈艳霞, 王乾, 魏冬铭, 梁淑君, 彭佳林, 易佳锦, 刘盛锋, 倪东, 王明亮, 张道强, 沈定刚*. 深度学习在医学影像中的应用综述. 中国图象图形学报. 2020, 25(10): 1953-1981. (获评2021年《中国图象图形学报》优秀论文)

[5] Xiangmin Han, Rundong Xue, Jingxi Feng, yifan feng, Shaoyi Du, Jun Shi*, Yue Gao*. Hypergraph foundation model for brain disease diagnosis. IEEE Transactions on Neural Networks and Learning Systems. Accepted.

[6] Xueying Zhou, Ge Jin, Yang Liu, Juncheng Li, Jun Wang, Shihui Ying, Yanyan Zheng, Jun Shi*. Multi-resolution based dual-channel UNet with cross clique for medical image dense prediction. Expert Systems With Applications. 2025, 276: 127190.

[7] Jingbo He, Bo Peng, Shihui Ying, Juncheng Li, Yakang Dai*, Jun Shi*. Dual-domain spatial-temporal reconstruction network for reconstruction of cine CMR. Biomedical Signal Processing and Control. 2025, 107: 107836.

[8] Hao Zhang, Qi Wang, Jun Shi, Shihui Ying*, Zhijie Wen. Deep unfolding network with spatial alignment for multi-modal MRI reconstruction. Medical Image Analysis. 2025, 99: 103331.

[9] Jiale Dun, Jun Wang*, Juncheng Li, Qianhui Yang, Wenlong Hang, Xiaofeng Lu, Shihui Ying, Jun Shi. A trustworthy curriculum learning guided multi-target domain adaptation network for Autism Spectrum Disorder classification. IEEE Journal of Biomedical and Health Informatics. 2025, 29(1): 310-323.

[10] Saisai Ding, Juncheng Li, Jun Wang, Shihui Ying, Jun Shi*. Multimodal co-attention fusion network with online data augmentation for cancer subtype classification. IEEE Transactions on Medical Imaging. 2024, 43(11): 3977-3989.

[11] Jian Wang, Liang Qiao, Shichong Zhou, Jin Zhou, Jun Wang, Juncheng Li, Shihui Ying, Cai Chang, Jun Shi*. Weakly supervised lesion detection and diagnosis for breast cancers with partially annotated ultrasound images. IEEE Transactions on Medical Imaging. 2024, 43(7): 2509-2521.

[12] Yuanming Zhang, Zheng Li, Xiangmin Han, Saisai Ding, Juncheng Li, Jun Wang, Shihui Ying, Jun Shi*. Pseudo-data based self-supervised federated learning for classification of histopathological images. IEEE Transactions on Medical Imaging. 2024, 43(3): 902-915.

[13] Qi Wang, Zhijie Wen, Jun Shi, Qian Wang, Dinggang Shen, Shihui Ying*. Spatial and modal optimal transport for fast cross-modal MRI reconstruction. IEEE Transactions on Medical Imaging. 2024, 43(11): 3924-3935.

[14] Tianxiang Huang, Jing Shi*, Juncheng Li, Jun Wang, Jun Du, Jun Shi*. Involution Transformer based U-Net for landmark detection in ultrasound images for diagnosis of infantile DDH. IEEE Journal of Biomedical and Health Informatics. 2024, 28(8): 4797-4809.

[15] Ke Sun, Jing Shi*, Ge Jin, Juncheng Li, Jun Wang, Jun Du, Jun Shi*. Dual-domain MIM based contrastive learning for CAD of developmental dysplasia of the hip with ultrasound images. Biomedical Signal Processing and Control. 2024, 97: 106684.

[16] Huili Zhang, Lehang Guo, Juncheng Li, Jun Wang, Shihui Ying, Jun Shi*. Multi-view disentanglement-based bidirectional generalized distillation for diagnosis of liver cancers with ultrasound images. Information Processing and Management. 2024, 61: 103855.

[17] Xiao Wang, Xinping Ren, Ge Jin, Shihui Ying, Jun Wang, Juncheng Li, Jun Shi*. B-mode ultrasound-based CAD by learning using privileged information with dual-level missing modality completion. Computers in Biology and Medicine. 2024, 182: 109106.

[18] Yinghua Fu, Junfeng Liu, Jun Shi*. TSCA-Net: Transformer based spatial-channel attention segmentation network for medical images. Computers in Biology and Medicine. 2024, 170: 107938.

[19] Jiashi Cao, Qiong Li, Huili Zhang, Yanyan Wu, Xiang Wang, Saisai Ding, Song Chen, Shaochun Xu, Guangwen Duan, Defu Qiu, Jiuyi Sun, Jun Shi*, Shiyuan Liu*. Radiomics model based on MRI to differentiate spinal multiple myeloma from metastases: A two-center study. Journal of Bone Oncology. 2024, 45: 100599.

[20] Yan Hu, Jun Wang*, Hao Zhu, Juncheng Li, Jun Shi. Cost-sensitive weighted contrastive learning based on graph convolutional networks for imbalanced Alzheimer’s disease staging. IEEE Transactions on Medical Imaging. 2024, 43(9): 3126-3136.

[21] Lipeng Cai, Jun Shi, Shaovi Du, Yue Gao, Shihui Ying*. Self-adaptive subspace representation from a geometric intuition. Pattern Recognition. 2024, 149: 110228.

[22] Bodong Cheng, Juncheng Li*, Jun Shi, Yingying Fang, Guixu Zhang, Yin Chen, Tieyong Zeng, Zhi Li*. WeaFU: Weather-informed image blind restoration via multi-weather distribution diffusion. IEEE Transactions on Circuits and Systems for Video Technology. 2024, 34(12): 13530-13542.

[23] Juncheng Li, Bodong Cheng, Ying Chen, Guangwei Gao, Jun Shi, Tieyong Zeng. EWT: Efficient Wavelet-Transformer for single image denoising. Neural Networks. 2024, 177: 106378.

[24] Yang Zhao, Bodong Cheng, Najun Niu, Jun Wang, Tieyong Zeng, Guixu Zhang, Jun Shi, Juncheng Li*. Few sampling meshes-based 3D tooth segmentation via region-aware graph convolutional network. Expert Systems With Applications. 2024, 252: 124255.

[25] Juncheng Li*, Hanhui Yang, Lok Ming Lui, Guixu Zhang, Jun Shi, Tieyong Zeng. A lightweight self-ensemble feedback recurrent network for Fast MRI reconstruction. International Journal of Machine Learning and Cybernetics. 2024.

[26] Juncheng Li, Bodong Cheng, Najun Niu, Guangwei Gao, Shihui Ying, Jun Shi, Tieyong Zeng. Fine-grained orthodontics segmentation model for 3D intraoral scan data. Computers in Biology and Medicine. 2024, 168: 107821.

[27] 赵阳, 李俊诚*, 成博栋, 牛娜君, 王龙光, 高广谓, 施俊. 深度学习在口腔医学影像中的应用与挑战. 中国图象图形学报. 2024, 29(3): 586-607.

[28] Saisai Ding, Juncheng Li, Jun Wang, Shihui Ying, Jun Shi*. Multi-scale efficient graph-Transformer for whole slide image classification. IEEE Journal of Biomedical and Health Informatics. 2023, 27(12): 5926-5936.

[29] Ronglin Gong, Jing Shi, Jian Wang, Jun Wang, Jianwei Zhou, Xiaofeng Lu, Jun Du*, Jun Shi*. Hybrid-supervised bidirectional transfer networks for computer-aided diagnosis. Computers in Biology and Medicine. 2023, 65: 107409.

[30] Xiangmin Han, Bangming Gong, Lehang Guo*, Jun Wang, Shihui Ying, Shuo Li, Jun Shi*. B-Mode ultrasound based CAD for liver cancers via multi-view privileged information learning. Neural Networks. 2023, 164: 369-381.

[31] Zhiyang Lu, Jian Wang, Zheng Li, Shihui Ying, Jun Wang, Jun Shi*, Dinggang Shen*. Two-stage self-supervised cycle-consistency Transformer network for reducing slice gap in MR images. IEEE Journal of Biomedical and Health Informatics. 2023, 27(7): 3337-3348.

[32] Zheng Li, Shihui Ying, Jun Wang, Hongjian He, Jun Shi*. Reconstruction of quantitative susceptibility mapping from total field maps with local field maps guided UU-Net. IEEE Journal of Biomedical and Health Informatics. 2023, 27(4): 2047-2058.

[33] Huili Zhang, Lehang Guo, Jun Wang, Shihui Ying, Jun Shi*. Multi-view feature transformation based SVM+ for computer-aided diagnosis of liver cancers with ultrasound images. IEEE Journal of Biomedical and Health Informatics. 2023, 27(3): 1512-1523.

[34] Xiangmin Han, Jun Wang, Shihui Ying, Jun Shi*, Dinggang Shen*. ML-DSVM+: a meta-learning based deep SVM+ for computer-aided diagnosis. Pattern Recognition. 2023, 134: 109076.

[35] Saisai Ding, Zhiyang Gao, Jun Wang, Minhua Lu*, Jun Shi*. Fractal graph convolutional network with MLP-mixer based multi-path feature fusion for classification of histopathological images. Expert Systems With Applications. 2023, 212: 118793.

[36] Guodong Chen, Zheng Li, Jian Wang, Jun Wang, Shisuo Du, Jinghao Zhou, Jun Shi*, Yongkang Zhou*. An improved 3D KiU-Net for segmentation of liver tumor. Computers in Biology and Medicine. 2023, 160: 107006.

[37] Jiaxin Huang#, Jun Shi#, Saisai Ding, Huili Zhang, Xueyan Wang, Shiyang Lin, Yanfen Xu, Mingjie Wei, Longzhong Liu, Xiaoqing Pei*. Deep learning model based on dual-modal ultrasound and molecular data for predicting response to neoadjuvant chemotherapy in breast cancer. Academic Radiology. 2023, 30: S50-S61.

[38] Chunxiao Lai, Huili Zhang, Jing Chen, Sihui Shao, Xin Li, Minghua Yao, Yi Zheng, Rong Wu*, Jun Shi*. Deep learning radiomics of ultrasonography for differentiating sclerosing adenosis from breast cancer. Clinical Hemorheology and Microcirculation. 2023, 84: 153-163.

[39] Qiong Wu, Jun Wang*, Zongqiong Sun, Lei Xiao, Wenhao Ying, Jun Shi. Immunotherapy efficacy prediction for non-small cell lung cancer using multi-view adaptive weighted graph convolutional networks. IEEE Journal of Biomedical and Health Informatics. 2023, 27(11): 5564-5575.

[40] Hao Zhu, Jun Wang, Yinping Zhao, Minhua Lu, Jun Shi. Contrastive multi-view composite graph convolutional networks based on contribution learning for Autism Spectrum disorder classification. IEEE Transactions on Biomedical Engineering. 2023, 70(6): 1943-1954.

[41] Zhaowu Lu, Jun Wang, Rui Mao, Minhua Lu, Jun Shi. Jointly composite feature learning and Autism spectrum disorder classification using deep multi-output Takagi-Sugeno-Kang fuzzy inference systems. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2023, 20(1): 476-488.

[42] Xin Wang, Jun Wang, Fei Shan, Yiqiang Zhan, Jun Shi, Dinggang Shen. Severity prediction of pulmonary diseases using chest CT scans via cost-sensitive label multi-kernel distribution learning. Computers in Biology and Medicine. 2023, 159: 106890.

[43] Hanhui Yang, Juncheng Li, Lok Ming Lui, Shihui Ying, Jun Shi, Tieyong Zeng. Fast MRI reconstruction via edge attention. Communications in Computational Physics. 2023, 33(5): 1409-1431.

[44] Jinhe Dong, Jun Shi, Yue Gao, Shihui Ying. GAME: Gaussian mixture error based meta learning architecture. Neural Computing and Applications. 2023, 35, 20445-20461.

[45] Hanlin Xu, Bohan Zhang, Yaxin Chen, Fengzhen Zeng, Wenjuan Wang, Ziyi Chen, Ling Cao, Jun Shi, Jun Chen, Xiaoxia Zhu, Yu Xue, Rui He, Minbiao Ji, Yinghui Hua. Type II collagen facilitates gouty arthritis by regulating MSU crystallization and inflammatory cell recruitments. Annals of the Rheumatic Diseases. 2023, 82(3): 416-427.

[46] Xiangmin Han, Xiaoyan Fei, Jun Wang, Tao Zhou, Shihui Ying, Jun Shi*, Dinggang Shen*. Doubly supervised transfer classifier for computer-aided diagnosis with imbalanced modalities. IEEE Transactions on Medical Imaging. 2022, 41(8): 2009-2020.

[47] Jun Wang, Fengyexin Zhang, Xiuyi Jia, Xin Wang, Han Zhang, Shihui Ying, Qian Wang, Jun Shi*, Dinggang Shen*. Multi-class ASD classification via label distribution learning with class-shared and class-specific decomposition. Medical Image Analysis. 2022, 75: 102294.

[48] Yanbin He, Zhiyang Lu, Jun Wang, Shihui Ying, Jun Shi*. A self-supervised learning based channel attention MLP-Mixer network for motor imagery decoding. IEEE Transactions on Neural Systems & Rehabilitation Engineering. 2022, 30: 2406-2417.

[49] Zhiyang Lu, Jun Li, Chaoyue Wang, Rongjun Ge, Lili Chen, Hongjian He, Jun Shi*. S2Q-Net: mining the high-pass filtered phase data in susceptibility weighted imaging for quantitative susceptibility mapping. IEEE Journal of Biomedical and Health Informatics. 2022, 26(8): 3938-3949.

[50] Zhiyang Gao, Zhiyang Lu, Jun Wang, Shihui Ying, Jun Shi*. A convolutional neural network and graph convolutional network based framework for classification of breast histopathological images. IEEE Journal of Biomedical and Health Informatics. 2022, 26(7): 3163-3173.

[51] Ronglin Gong, Xiangmin Han, Jun Wang, Shihui Ying, Jun Shi*. Self-supervised bi-channel Transformer networks for computer-aided diagnosis. IEEE Journal of Biomedical and Health Informatics. 2022, 26(7): 3435-3446.

[52] Bangming Gong, Jing Shi, Xiangmin Han, Huan Zhang, Yuemin Huang, Liwei Hu, Jun Wang, Jun Du*, Jun Shi*. Diagnosis of infantile hip dysplasia with B-mode ultrasound via two-stage meta-learning based deep exclusivity regularized machine. IEEE Journal of Biomedical and Health Informatics. 2022, 26(1): 334-344.

[53] Ronglin Gong, Linlin Wang, Jun Wang, Binjie Ge, Hang Yu, Jun Shi*. Self-distilled supervised contrastive learning for diagnosis of breast cancers with histopathological images. Computers in Biology and Medicine. 2022, 146: 105641.

[54] Weijie Kang, Min Ji, Huili Zhang, Hua Shi, Tianchao Xiang, Yaqi Li, Ye Fang, Qi Qi, Junbo Wang, Jian Shen, Liangfeng Tang, Xiaoxiong Liu, Yingzi Ye, Xiaoling Ge, Xiang Wang, Hong Xu, Zhongwei Qiao*, Jun Shi*, Jia Rao*. A novel clinical-radiomics model predicted renal lesions and deficiency in children on diffusion-weighted MRI. Frontiers in Physics. 2022.

[55] Jun Wang, Zhuangzhuang Zhao, Zhaohong Deng, Kup-Sze Choi, Lejun Gong, Jun Shi, Shitong Wang. Manifold-regularized multitask fuzzy system modeling with low-rank and sparse structures in consequent parameters. IEEE Transactions on Fuzzy Systems. 2022, 30(5): 1486-1500.

[56] Weichang Ding, Jun Wang*, Weijun Zhou, Shichong Zhou, Cai Chang, Jun Shi. Joint localization and classification of breast cancer in B-mode ultrasound imaging via collaborative learning with elastography. IEEE Journal of Biomedical and Health Informatics. 2022, 26(9): 4474-4485.

[57] 贡荣麟, 施俊*, 周玮珺, 汪程. 面向乳腺超声计算机辅助诊断的两阶段深度迁移学习. 中国图象图形学报. 2022, 27(3): 898-910.

[58] Xing Wu*, Cheng Chen, Mingyu Zhong, Jianjia Wang, Jun Shi*. COVID-AL: the diagnosis of COVID-19 with deep active learning. Medical Image Analysis. 2021, 63: 101913.

[59] Xiaoyan Fei, Shichong Zhou, Xiangmin Han, Jun Wang, Shihui Ying, Cai Chang, Weijun Zhou, Jun Shi*. Doubly supervised parameter transfer classifier for diagnosis of breast cancer with imbalanced ultrasound imaging modalities. Pattern Recognition. 2021, 120: 108139.

[60] Huili Zhang, Lehang Guo, Dan Wang, Jun Wang, Lili Bao, Shihui Ying, Huixiong Xu*, Jun Shi*. Multi-source transfer learning via multi-kernel support vector machine plus for B-mode ultrasound-based computer-aided diagnosis of liver cancers. IEEE Journal of Biomedical and Health Informatics. 2021, 25(10): 3874-3885.

[61] Zheng Li, Jun Li, Chaoyue Wang, Zhiyang Lu, Jun Wang, Hongjian He*, Jun Shi*. Meta-learning based interactively connected clique U-Net for quantitative susceptibility mapping. IEEE Transactions on Computational Imaging. 2021, 7: 1385-1399.

[62] Zheng Li, Chaofeng Wang, Jun Wang, Shihui Ying, Jun Shi*. Lightweight adaptive weighted network for single image super-resolution. Computer Vision and Image Understanding. 2021, 211: 103254.

[63] Shanshan Wang#, Guohua Cao#, Yan Wang#, Shu Liao#, Qian Wang#, Jun Shi#, Cheng Li, Dinggang Shen*. Review and prospect: artificial intelligence in advanced medical imaging. Frontiers in Radiology. 2021, 1: 781868.

[64] 应时辉、杨菀、杜少毅、施俊*. 基于深度学习的医学影像配准综述. 模式识别与人工智能. 2021, 34(4): 287-299.

[65] Weiwen Wu, Jun Shi, Hengyong Yu, Weifei Wu*, Varut Vardhanabhuti*. Tensor gradient L0-norm minimization based low-dose CT and its application to COVID-19. IEEE Transactions on Instrumentation & Measurement. 2021, 70: 4503012.

[66] 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. 2020, 39(10): 3137-3147.

[67] 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.

[68] 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.

[69] 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.

[70] 贡荣麟, 施俊*, 王骏. 基于混合监督双通道反馈U-Net的乳腺超声图像分割. 中国图象图形学报. 2020, 25(10): 2206-2217.

[71] 沈璐, 王倩婷, 施俊*. 基于特权信息集成学习的精神分裂症单模态神经影像计算机辅助诊断. 生物医学工程学杂志, 2020, 37(3): 405-411.

[72] 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.

[73] 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.

[74] 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.

[75] 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.

[76] 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.

[77] 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.

[78] 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.

[79] 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.

[80] 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.

[81] 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.

[82] 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.

[83] 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.

[84] 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.

[85] 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.

[86] 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.

[87] 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.

[88] 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.

[89] 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.

[90] 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.

[91] 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.

[92] 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.

[93] 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.

[94] 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.

[95] 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.

[96] Jun Shi, Qikun Jiang, Rui Mao, Minhua Lu, Tianfu Wang. FR-KECA: fuzzy robust kernel entropy component analysis. Neurocomputing. 2015, 149: 1415-1423.

[97] 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.

[98] Qi Zhang, Chaolun Li, Hong Han, Wei Dai, Jun Shi, Yuanyuan Wang, Wenping Wang.  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.

[99] 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.

[100] Huali Chang, Zhenping Chen, Qinghua Huang, Jun Shi, Xuelong Li. Graph-based learning for segmentation of 3D ultrasound images. Neurocomputing. 2015, 151: 632-644.

[101] 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.

[102] 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.

[103] 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.

[104] 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.

[105] 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.

[106] 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.

[107] 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.

[108] 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.

[109] 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.

[110] 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.

[111] 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.

[112] 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.

[113] 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.

代表性会议论文：

[1] Tianxiang Huang, Jing Shi, Ge Jin, Juncheng Li, Jun Wang, Jun Du*, Jun Shi*. Topological GCN for improving detection of hip landmarks from B-mode ultrasound Images. The 27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). 2024 (Oral presentation, 2.7%).

[2] Saisai Ding, Jun Wang, Juncheng Li, Jun Shi*. Multi-scale prototypical Transformer for whole slide image classification. The 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). 2023.

[3] Yanbin He, Zhiyang Lu, Jun Wang, Jun Shi*. A channel attention based MLP-Mixer network for motor imagery decoding with EEG. 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2022.

[4] Ronglin Gong, Shihui Ying, Jun Shi*. Task-driven self-supervised bi-channel networks learning for diagnosis of breast cancers with mammography. 2022 IEEE International Conference in Image Processing (ICIP). 2022.

[5] Zhiyang Gao, Jun Wang, Jun Shi*. GQ-GCN: Group quadratic graph convolution network for classification of histopathological images. The 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). 2021.

[6] Zhiyang Lu, Zheng Li, Jun Wang, Jun Shi*, Dinggang Shen*. Two-stage self-supervised cycle-consistency network for reconstruction of thin-slice MR images. The 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). 2021.

[7] 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). 2021.

[8] 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 23rf International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). 2020.

[9] 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.

[10] 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)

[11] Jun Wang, Ying Zhang, Tao Zhou, Zhaohong Deng, Huifang Huang, Shitong Wang, Jun Shi, Dinggang Shen. Interpretable feature learning using multi-output Takagi-Sugeno-Kang fuzzy system for multi-center ASD diagnosis. The 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). 2019.

[12] 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.

[13] 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)

[14] 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)

[15] 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)

[16] 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.

[17] 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.

[18] 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.

[19] 钱奕奕, 施俊*, 郑晓, 张麒, 郭乐航, 王丹, 徐辉雄. 基于多模态超声成像的肝肿瘤计算机辅助诊断. 2017中国生物医学工程大会，青年论文竞赛三等奖. (Oral Representation)

[20] 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. 2017 IEEE International Symposium on Biomedical Imaging (ISBI). 2017. (Oral Representation)

[21] 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)

[22] Yiyi Qian, Jun Shi*, Xiao Zheng, Qi Zhang, Lehang Guo, Dan Wang, 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)

[23] 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)

[24] 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)

[25] Xiao Zheng, Jun Shi*, Yan Li, Xiao Liu, Qi Zhang. Multi-modality stacked deep polynomial network based feature learning for Alzheimer’s disease diagnosis. 2016 IEEE International Symposium on Biomedical Imaging (ISBI). 2016.

[26] 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.

[27] 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.

[28] 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.

[29] Jie Zhu, Jun Shi*. Hessian regularization based semi-supervised dimensionality reduction for neuroimaging data of Alzheimer’s disease. 2014 IEEE International Symposium on Biomedical Imaging (ISBI). 2014.

[30] 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.

[31] 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.

[32] 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.

 

 

 

 

  

 

中 文

Jun Shi, Ph.D, Professor, Deputy Dean

Office：

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

Mail Address(Zip Code)：

Box 83, 99 Shangda Road, Shanghai University, Shanghai (200444)

Phone：

86-21-66137269/66138178

Email：

junshi@shu.edu.cn

URL：

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

Research Interests:

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

Educational Background:

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

Working Experiences:

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), Key Program of NSFC (Co-PI), Special Fund for Basic Research on Scientific Instruments 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] 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. 2021, 14: 4-15. (#Equal Contribution, Highly Cited Paper)

[3] Zhongyi Hu, Jun Wang, Chunxiang Zhang, Zhenzhen Luo, Xiaoqing Luo, Lei Xiao, Jun Shi. Uncertainty modeling for multi-center Autism Spectrum Disorder classification using Takagi-Sugeno-Kang fuzzy systems. IEEE Transactions on Cognitive and Developmental Systems. 2022, 14(2): 730-739. (Highly Cited Paper)

[4] Xiangmin Han, Rundong Xue, Jingxi Feng, yifan feng, Shaoyi Du, Jun Shi*, Yue Gao*. Hypergraph foundation model for brain disease diagnosis. IEEE Transactions on Neural Networks and Learning Systems. Accepted.

[5] Xueying Zhou, Ge Jin, Yang Liu, Juncheng Li, Jun Wang, Shihui Ying, Yanyan Zheng, Jun Shi*. Multi-resolution based dual-channel UNet with cross clique for medical image dense prediction. Expert Systems With Applications. 2025, 276: 127190.

[6] Jingbo He, Bo Peng, Shihui Ying, Juncheng Li, Yakang Dai*, Jun Shi*. Dual-domain spatial-temporal reconstruction network for reconstruction of cine CMR. Biomedical Signal Processing and Control. 2025, 107: 107836.

[7] Hao Zhang, Qi Wang, Jun Shi, Shihui Ying*, Zhijie Wen. Deep unfolding network with spatial alignment for multi-modal MRI reconstruction. Medical Image Analysis. 2025, 99: 103331.

[8] Jiale Dun, Jun Wang*, Juncheng Li, Qianhui Yang, Wenlong Hang, Xiaofeng Lu, Shihui Ying, Jun Shi. A trustworthy curriculum learning guided multi-target domain adaptation network for Autism Spectrum Disorder classification. IEEE Journal of Biomedical and Health Informatics. 2025, 29(1): 310-323.

[9] Saisai Ding, Juncheng Li, Jun Wang, Shihui Ying, Jun Shi*. Multimodal co-attention fusion network with online data augmentation for cancer subtype classification. IEEE Transactions on Medical Imaging. 2024, 43(11): 3977-3989.

[10] Jian Wang, Liang Qiao, Shichong Zhou, Jin Zhou, Jun Wang, Juncheng Li, Shihui Ying, Cai Chang, Jun Shi*. Weakly supervised lesion detection and diagnosis for breast cancers with partially annotated ultrasound images. IEEE Transactions on Medical Imaging. 2024, 43(7): 2509-2521.

[11] Yuanming Zhang, Zheng Li, Xiangmin Han, Saisai Ding, Juncheng Li, Jun Wang, Shihui Ying, Jun Shi*. Pseudo-data based self-supervised federated learning for classification of histopathological images. IEEE Transactions on Medical Imaging. 2024, 43(3): 902-915.

[12] Qi Wang, Zhijie Wen, Jun Shi, Qian Wang, Dinggang Shen, Shihui Ying*. Spatial and modal optimal transport for fast cross-modal MRI reconstruction. IEEE Transactions on Medical Imaging. 2024, 43(11): 3924-3935.

[13] Tianxiang Huang, Jing Shi*, Juncheng Li, Jun Wang, Jun Du, Jun Shi*. Involution Transformer based U-Net for landmark detection in ultrasound images for diagnosis of infantile DDH. IEEE Journal of Biomedical and Health Informatics. 2024, 28(8): 4797-4809.

[14] Ke Sun, Jing Shi*, Ge Jin, Juncheng Li, Jun Wang, Jun Du, Jun Shi*. Dual-domain MIM based contrastive learning for CAD of developmental dysplasia of the hip with ultrasound images. Biomedical Signal Processing and Control. 2024, 97: 106684.

[15] Huili Zhang, Lehang Guo, Juncheng Li, Jun Wang, Shihui Ying, Jun Shi*. Multi-view disentanglement-based bidirectional generalized distillation for diagnosis of liver cancers with ultrasound images. Information Processing and Management. 2024, 61: 103855.

[16] Xiao Wang, Xinping Ren, Ge Jin, Shihui Ying, Jun Wang, Juncheng Li, Jun Shi*. B-mode ultrasound-based CAD by learning using privileged information with dual-level missing modality completion. Computers in Biology and Medicine. 2024, 182: 109106.

[17] Yinghua Fu, Junfeng Liu, Jun Shi*. TSCA-Net: Transformer based spatial-channel attention segmentation network for medical images. Computers in Biology and Medicine. 2024, 170: 107938.

[18] Jiashi Cao, Qiong Li, Huili Zhang, Yanyan Wu, Xiang Wang, Saisai Ding, Song Chen, Shaochun Xu, Guangwen Duan, Defu Qiu, Jiuyi Sun, Jun Shi*, Shiyuan Liu*. Radiomics model based on MRI to differentiate spinal multiple myeloma from metastases: A two-center study. Journal of Bone Oncology. 2024, 45: 100599.

[19] Yan Hu, Jun Wang*, Hao Zhu, Juncheng Li, Jun Shi. Cost-sensitive weighted contrastive learning based on graph convolutional networks for imbalanced Alzheimer’s disease staging. IEEE Transactions on Medical Imaging. 2024, 43(9): 3126-3136.

[20] Lipeng Cai, Jun Shi, Shaovi Du, Yue Gao, Shihui Ying. Self-adaptive subspace representation from a geometric intuition. Pattern Recognition. 2024, 149: 110228.

[21] Bodong Cheng, Juncheng Li*, Jun Shi, Yingying Fang, Guixu Zhang, Yin Chen, Tieyong Zeng, Zhi Li*. WeaFU: Weather-informed image blind restoration via multi-weather distribution diffusion. IEEE Transactions on Circuits and Systems for Video Technology. 2024, 34(12): 13530-13542.

[22] Juncheng Li, Bodong Cheng, Ying Chen, Guangwei Gao, Jun Shi, Tieyong Zeng. EWT: Efficient Wavelet-Transformer for single image denoising. Neural Networks. 2024, 177: 106378.

[23] Yang Zhao, Bodong Cheng, Najun Niu, Jun Wang, Tieyong Zeng, Guixu Zhang, Jun Shi, Juncheng Li*. Few sampling meshes-based 3D tooth segmentation via region-aware graph convolutional network. Expert Systems With Applications. 2024, 252: 124255.

[24] Juncheng Li*, Hanhui Yang, Lok Ming Lui, Guixu Zhang, Jun Shi, Tieyong Zeng. A lightweight self-ensemble feedback recurrent network for Fast MRI reconstruction. International Journal of Machine Learning and Cybernetics. 2024.

[25] Juncheng Li, Bodong Cheng, Najun Niu, Guangwei Gao, Shihui Ying, Jun Shi, Tieyong Zeng. Fine-grained orthodontics segmentation model for 3D intraoral scan data. Computers in Biology and Medicine. 2024, 168: 107821.

[26] Saisai Ding, Juncheng Li, Jun Wang, Shihui Ying, Jun Shi*. Multi-scale efficient graph-Transformer for whole slide image classification. IEEE Journal of Biomedical and Health Informatics. 2023, 27(12): 5926-5936.

[27] Ronglin Gong, Jing Shi, Jian Wang, Jun Wang, Jianwei Zhou, Xiaofeng Lu, Jun Du*, Jun Shi*. Hybrid-supervised bidirectional transfer networks for computer-aided diagnosis. Computers in Biology and Medicine. 2023, 65: 107409.

[28] Xiangmin Han, Bangming Gong, Lehang Guo*, Jun Wang, Shihui Ying, Shuo Li, Jun Shi*. B-Mode ultrasound based CAD for liver cancers via multi-view privileged information learning. Neural Networks. 2023, 164: 369-381.

[29] Zhiyang Lu, Jian Wang, Zheng Li, Shihui Ying, Jun Wang, Jun Shi*, Dinggang Shen*. Two-stage self-supervised cycle-consistency Transformer network for reducing slice gap in MR images. IEEE Journal of Biomedical and Health Informatics. 2023, 27(7): 3337-3348.

[30] Zheng Li, Shihui Ying, Jun Wang, Hongjian He, Jun Shi*. Reconstruction of quantitative susceptibility mapping from total field maps with local field maps guided UU-Net. IEEE Journal of Biomedical and Health Informatics. 2023, 27(4): 2047-2058.

[31] Huili Zhang, Lehang Guo, Jun Wang, Shihui Ying, Jun Shi*. Multi-view feature transformation based SVM+ for computer-aided diagnosis of liver cancers with ultrasound images. IEEE Journal of Biomedical and Health Informatics. 2023, 27(3): 1512-1523.

[32] Xiangmin Han, Jun Wang, Shihui Ying, Jun Shi*, Dinggang Shen*. ML-DSVM+: a meta-learning based deep SVM+ for computer-aided diagnosis. Pattern Recognition. 2023, 134: 109076.

[33] Saisai Ding, Zhiyang Gao, Jun Wang, Minhua Lu*, Jun Shi*. Fractal graph convolutional network with MLP-mixer based multi-path feature fusion for classification of histopathological images. Expert Systems With Applications. 2023, 212: 118793.

[34] Guodong Chen, Zheng Li, Jian Wang, Jun Wang, Shisuo Du, Jinghao Zhou, Jun Shi*, Yongkang Zhou*. An improved 3D KiU-Net for segmentation of liver tumor. Computers in Biology and Medicine. 2023, 160: 107006.

[35] Jiaxin Huang#, Jun Shi#, Saisai Ding, Huili Zhang, Xueyan Wang, Shiyang Lin, Yanfen Xu, Mingjie Wei, Longzhong Liu, Xiaoqing Pei*. Deep learning model based on dual-modal ultrasound and molecular data for predicting response to neoadjuvant chemotherapy in breast cancer. Academic Radiology. 2023, 30: S50-S61.

[36] Chunxiao Lai, Huili Zhang, Jing Chen, Sihui Shao, Xin Li, Minghua Yao, Yi Zheng, Rong Wu*, Jun Shi*. Deep learning radiomics of ultrasonography for differentiating sclerosing adenosis from breast cancer. Clinical Hemorheology and Microcirculation. 2023, 84: 153-163.

[37] Qiong Wu, Jun Wang*, Zongqiong Sun, Lei Xiao, Wenhao Ying, Jun Shi. Immunotherapy efficacy prediction for non-small cell lung cancer using multi-view adaptive weighted graph convolutional networks. IEEE Journal of Biomedical and Health Informatics. 2023, 27(11): 5564-5575.

[38] Hao Zhu, Jun Wang, Yinping Zhao, Minhua Lu, Jun Shi. Contrastive multi-view composite graph convolutional networks based on contribution learning for Autism Spectrum disorder classification. IEEE Transactions on Biomedical Engineering. 2023, 70(6): 1943 - 1954.

[39] Zhaowu Lu, Jun Wang, Rui Mao, Minhua Lu, Jun Shi. Jointly composite feature learning and Autism spectrum disorder classification using deep multi-output Takagi-Sugeno-Kang fuzzy inference systems. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2023, 20(1): 476-488.

[40] Xin Wang, Jun Wang, Fei Shan, Yiqiang Zhan, Jun Shi, Dinggang Shen. Severity prediction of pulmonary diseases using chest CT scans via cost-sensitive label multi-kernel distribution learning. Computers in Biology and Medicine. 2023, 159: 106890.

[41] Hanhui Yang, Juncheng Li, Lok Ming Lui, Shihui Ying, Jun Shi, Tieyong Zeng. Fast MRI reconstruction via edge attention. Communications in Computational Physics. 2023, 33(5): 1409-1431.

[42] Jinhe Dong, Jun Shi, Yue Gao, Shihui Ying. GAME: Gaussian mixture error based meta learning architecture. Neural Computing and Applications. 2023, 35: 20445-20461.

[43] Hanlin Xu, Bohan Zhang, Yaxin Chen, Fengzhen Zeng, Wenjuan Wang, Ziyi Chen, Ling Cao, Jun Shi, Jun Chen, Xiaoxia Zhu, Yu Xue, Rui He, Minbiao Ji, Yinghui Hua. Type II collagen facilitates gouty arthritis by regulating MSU crystallization and inflammatory cell recruitments. Annals of the Rheumatic Diseases. 2023, 82(3): 416-427.

[44] Xiangmin Han, Xiaoyan Fei, Jun Wang, Tao Zhou, Shihui Ying, Jun Shi*, Dinggang Shen*. Doubly supervised transfer classifier for computer-aided diagnosis with imbalanced modalities. IEEE Transactions on Medical Imaging. 2022, 41(8): 2009-2020.

[45] Jun Wang, Fengyexin Zhang, Xiuyi Jia, Xin Wang, Han Zhang, Shihui Ying, Qian Wang, Jun Shi*, Dinggang Shen*. Multi-class ASD classification via label distribution learning with class-shared and class-specific decomposition. Medical Image Analysis. 2022, 75: 102294.

[46] Yanbin He, Zhiyang Lu, Jun Wang, Shihui Ying, Jun Shi*. A self-supervised learning based channel attention MLP-Mixer network for motor imagery decoding. IEEE Transactions on Neural Systems & Rehabilitation Engineering. 2022, 30: 2406-2417.

[47] Zhiyang Lu, Jun Li, Chaoyue Wang, Rongjun Ge, Lili Chen, Hongjian He, Jun Shi*. S2Q-Net: mining the high-pass filtered phase data in susceptibility weighted imaging for quantitative susceptibility mapping. IEEE Journal of Biomedical and Health Informatics. 2022, 26(8): 3938-3949.

[48] Zhiyang Gao, Zhiyang Lu, Jun Wang, Shihui Ying, Jun Shi*. A convolutional neural network and graph convolutional network based framework for classification of breast histopathological images. IEEE Journal of Biomedical and Health Informatics. 2022, 26(7): 3163-3173.

[49] Ronglin Gong, Xiangmin Han, Jun Wang, Shihui Ying, Jun Shi*. Self-supervised bi-channel Transformer networks for computer-aided diagnosis. IEEE Journal of Biomedical and Health Informatics. 2022, 26(7): 3435-3446.

[50] Bangming Gong, Jing Shi, Xiangmin Han, Huan Zhang, Yuemin Huang, Liwei Hu, Jun Wang, Jun Du*, Jun Shi*. Diagnosis of infantile hip dysplasia with B-mode ultrasound via two-stage meta-learning based deep exclusivity regularized machine. IEEE Journal of Biomedical and Health Informatics. 2022, 26(1): 334-344.

[51] Ronglin Gong, Linlin Wang, Jun Wang, Binjie Ge, Hang Yu, Jun Shi*. Self-distilled supervised contrastive learning for diagnosis of breast cancers with histopathological images. Computers in Biology and Medicine. 2022, 146:105641.

[52] Weijie Kang, Min Ji, Huili Zhang, Hua Shi, Tianchao Xiang, Yaqi Li, Ye Fang, Qi Qi, Junbo Wang, Jian Shen, Liangfeng Tang, Xiaoxiong Liu, Yingzi Ye, Xiaoling Ge, Xiang Wang, Hong Xu, Zhongwei Qiao*, Jun Shi*, Jia Rao*. A novel clinical-radiomics model predicted renal lesions and deficiency in children on diffusion-weighted MRI. Frontiers in Physics. 2022.

[53] Jun Wang, Zhuangzhuang Zhao, Zhaohong Deng, Kup-Sze Choi, Lejun Gong, Jun Shi, Shitong Wang. Manifold-regularized multitask fuzzy system modeling with low-rank and sparse structures in consequent parameters. IEEE Transactions on Fuzzy Systems. 2022, 30(5): 1486-1500.

[54] Weichang Ding, Jun Wang, Weijun Zhou, Shichong Zhou, Cai Chang, Jun Shi. Joint localization and classification of breast cancer in B-mode ultrasound imaging via collaborative learning with elastography. IEEE Journal of Biomedical and Health Informatics. 2022, 26(9): 4474-4485.

[55] Xing Wu*, Cheng Chen, Mingyu Zhong, Jianjia Wang, Jun Shi*. COVID-AL: the diagnosis of COVID-19 with deep active learning. Medical Image Analysis. 2021, 63: 101913.

[56] Xiaoyan Fei, Shichong Zhou, Xiangmin Han, Jun Wang, Shihui Ying, Cai Chang, Weijun Zhou, Jun Shi*. Doubly supervised parameter transfer classifier for diagnosis of breast cancer with imbalanced ultrasound imaging modalities. Pattern Recognition. 2021, 120: 108139.

[57] Huili Zhang, Lehang Guo, Dan Wang, Jun Wang, Lili Bao, Shihui Ying, Huixiong Xu*, Jun Shi*. Multi-source transfer learning via multi-kernel support vector machine plus for B-mode ultrasound-based computer-aided diagnosis of liver cancers. IEEE Journal of Biomedical and Health Informatics. 2021, 25(10): 3874-3885.

[58] Zheng Li, Jun Li, Chaoyue Wang, Zhiyang Lu, Jun Wang, Hongjian He*, Jun Shi*. Meta-learning based interactively connected clique U-Net for quantitative susceptibility mapping. IEEE Transactions on Computational Imaging. 2021, 7: 1385-1399.

[59] Zheng Li, Chaofeng Wang, Jun Wang, Shihui Ying, Jun Shi*. Lightweight adaptive weighted network for single image super-resolution. Computer Vision and Image Understanding. 2021, 211: 103254.

[60] Shanshan Wang#, Guohua Cao#, Yan Wang#, Shu Liao#, Qian Wang#, Jun Shi#, Cheng Li, Dinggang Shen*. Review and prospect: artificial intelligence in advanced medical imaging. Frontiers in Radiology. 2021, 1: 781868.

[61] Weiwen Wu, Jun Shi, Hengyong Yu, Weifei Wu*, Varut Vardhanabhuti*. Tensor gradient L0-norm minimization based low-dose CT and its application to COVID-19. IEEE Transactions on Instrumentation & Measurement. 2021, 70: 4503012.

[62] Jun Wang, Lichi Zhang, Qian Wang*, Lei Chen, Jun Shi, Xiaobo Chen, Zuoyong Li, 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. 2020, 39(10): 3137-3147.

[63] 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.

[64] 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.

[65] 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.

[66] 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.

[67] 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.

[68] 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.

[69] 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.

[70] 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.

[71] 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.

[72] 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.

[73] 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.

[74] 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.

[75] 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.

[76] 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.

[77] 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.

[78] 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.

[79] 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.

[80] 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.

[81] 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.

[82] 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.

[83] 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.

[84] 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.

[85] 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.

[86] 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.

[87] 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.

[88] 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.

[89] 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.

[90] Jun Shi, Qikun Jiang, Rui Mao, Minhua Lu, Tianfu Wang. FR-KECA: fuzzy robust kernel entropy component analysis. Neurocomputing. 2015, 149: 1415-1423.

[91] 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.

[92] Qi Zhang, Chaolun Li, Hong Han, Wei Dai, Jun Shi, Yuanyuan Wang, Wenping Wang.  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.

[93] 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.

[94] Huali Chang, Zhenping Chen, Qinghua Huang, Jun Shi, Xuelong Li. Graph-based learning for segmentation of 3D ultrasound images. Neurocomputing. 2015, 151: 632-644.

[95] 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.

[96] 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.

[97] 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.

[98] 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.

[99] 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.

[100] 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.

[101] 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.

[102] 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.

[103] 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.

[104] 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.

[105] 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.

[106] 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.

[107] 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] Tianxiang Huang, Jing Shi, Ge Jin, Juncheng Li, Jun Wang, Jun Du*, Jun Shi*. Topological GCN for improving detection of hip landmarks from B-mode ultrasound Images. The 27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). 2024 (Oral presentation, 2.7%).

[2] Saisai Ding, Jun Wang, Juncheng Li, Jun Shi*. Multi-scale prototypical Transformer for whole slide image classification. The 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). 2023.

[3] Yanbin He, Zhiyang Lu, Jun Wang, Jun Shi*. A channel attention based MLP-Mixer network for motor imagery decoding with EEG. 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2022.

[4] Ronglin Gong, Shihui Ying, and Jun Shi*. Task-driven self-supervised bi-channel networks learning for diagnosis of breast cancers with mammography. 2022 IEEE International Conference in Image Processing (ICIP). 2022.

[5] Zhiyang Gao, Jun Wang, Jun Shi*. GQ-GCN: Group quadratic graph convolution network for classification of histopathological images. The 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). 2021.

[6] Zhiyang Lu, Zheng Li, Jun Wang, Jun Shi*, Dinggang Shen*. Two-stage self-supervised cycle-consistency network for reconstruction of thin-slice MR images. The 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). 2021.

[7] 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). 2021.

[8] 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 23rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). 2020.

[9] 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.

[10] 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)

[11] Jun Wang, Ying Zhang, Tao Zhou, Zhaohong Deng, Huifang Huang, Shitong Wang, Jun Shi, Dinggang Shen. Interpretable feature learning using multi-output Takagi-Sugeno-Kang fuzzy system for multi-center ASD diagnosis. The 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). 2019.

[12] 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.

[13] 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)

[14] 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)

[15] 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)

[16] 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.

[17] 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.

[18] 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.

[19] 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. 2017 IEEE International Symposium on Biomedical Imaging (ISBI). 2017. (Oral Representation)

[20] 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)

[21] Yiyi Qian, Jun Shi*, Xiao Zheng, Qi Zhang, Lehang Guo, Dan Wang, 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)

[22] 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)

[23] 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)

[24] Xiao Zheng, Jun Shi*, Yan Li, Xiao Liu, Qi Zhang. Multi-modality stacked deep polynomial network based feature learning for Alzheimer’s disease diagnosis. 2016 IEEE International Symposium on Biomedical Imaging (ISBI). 2016.

[25] 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.

[26] 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.

[27] 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.

[28] Jie Zhu, Jun Shi*. Hessian regularization based semi-supervised dimensionality reduction for neuroimaging data of Alzheimer’s disease. 2014 IEEE International Symposium on Biomedical Imaging (ISBI). 2014.

[29] 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.

[30] 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.

[31] 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.