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施俊 博士，教授，博导，副院长 |
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办公室： |
上海大学南陈路333号翔英大楼825室 |
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通信地址(邮政编码): |
上海市上大路99号83信箱（200444） |
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电话： |
021-66137269，021-66138178 |
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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，十大网赌博正规信誉网址，上海先进通信与数据科学研究院，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、上海市卫生局项目、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, Hot Paper, 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] 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.
Accepted. [6] 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. Accepted. [7] 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. Accepted. [8] Hanhui Yang, Juncheng Li, Lok
Ming Lui, Shihui Ying, Jun Shi,
Tieyong Zeng. Fast MRI reconstruction via edge attention. Communications in Computational Physics.
Accepted. [9] 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. Accepted. [10] 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. [11] 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. [12] 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. [13] 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. [14] 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. [15] 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. [16] 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. [17] 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. [18] Chunxiao Lia, 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. [19] 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. [20] 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. [21] 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. [22] Jinhe Dong, Jun Shi, Yue Gao, Shihui Ying. GAME: Gaussian mixture
error based meta learning architecture. Neural
Computing and Applications. 2023. [23] 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. [24] 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. [25] 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. [26] 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. [27] 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. [28] 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. [29] 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. [30] 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. [31] 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. [32] 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. [33] 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. [34] 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. [35] 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. [36] 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. [37] 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. [38] 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. [39] 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. [40] 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. [41] 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. [42] 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. [43] 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. [44] 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. [45] 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. [46] 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. [47] 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. [48] 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. [49] 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. [50] 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. [51] 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. [52] 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. [53] 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. [54] 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. [55] 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. [56] 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. [57] 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. [58] 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. [59] 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. [60] 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. [61] 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. [62] 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. [63] 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. [64] 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. [65] 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. [66] 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. [67] 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. [68] 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. [69] 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. [70] 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. [71] 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. [72] 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. [73] Jun
Shi,
Qikun Jiang, Rui Mao, Minhua Lu, Tianfu Wang. FR-KECA: fuzzy robust kernel entropy
component analysis. Neurocomputing.
2015, 149: 1415-1423. [74] 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. [75] 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. [76] 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. [77] Huali Chang, Zhenping Chen, Qinghua
Huang, Jun Shi, Xuelong Li.
Graph-based learning for segmentation of 3D ultrasound images. Neurocomputing. 2015, 151: 632-644. [78] 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. [79] 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. [80] 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. [81] 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. [82] 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. [83] 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. [84] 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. [85] 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. [86] 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. [87] 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. [88] 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. [89] 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. [90] 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] 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. [2] 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] 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. [5] 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. [6] 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. [7] 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. [8] 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. [9] 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) [10] 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. [11] 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. [12] 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) [13] 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) [14] 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) [15] 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. [16] 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. [17] 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. [18] 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) [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. |
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Jun Shi, Ph.D, Professor，Deputy Dean |
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Office： |
825
Xiangying Building, 333 Nanchen Road, Shanghai University, Shanghai |
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Mail Address(Zip Code)： |
Box 83, 99 Shangda Road, Shanghai University,
Shanghai (200444) |
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Phone： |
86-21-66137269，86-21-66138178 |
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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, Hot Paper, 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] 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.
Accepted. [5] 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.
Accepted. [6] 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. Accepted. [7] Hanhui Yang, Juncheng Li, Lok
Ming Lui, Shihui Ying, Jun Shi,
Tieyong Zeng. Fast MRI reconstruction via edge attention. Communications in Computational Physics.
Accepted. [8] 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. Accepted. [9] 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. [10] 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. [11] 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. [12] 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. [13] 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. [14] 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. [15] 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. [16] 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. [17] Chunxiao Lia, 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. [18] 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. [19] 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. [20] 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. [21] Jinhe Dong, Jun Shi, Yue Gao, Shihui Ying. GAME: Gaussian mixture
error based meta learning architecture. Neural
Computing and Applications. 2023. [22] 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. [23] 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. [24] 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. [25] 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. [26] 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. [27] 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. [28] 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. [29] 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. [30] 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. [31] 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. [32] 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. [33] 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. [34] 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. [35] 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. [36] 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. [37] 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. [38] 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. [39] 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. [40] 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. [41] 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. [42] 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. [43] 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. [44] 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. [45] 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. [46] 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. [47] 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. [48] 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. [49] 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. [50] 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. [51] 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. [52] 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. [53] 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. [54] 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. [55] 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. [56] 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. [57] 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. [58] 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. [59] 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. [60] 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. [61] 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. [62] 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. [63] 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. [64] 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. [65] 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. [66] 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. [67] 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. [68] Jun
Shi,
Qikun Jiang, Rui Mao, Minhua Lu, Tianfu Wang. FR-KECA: fuzzy robust kernel entropy
component analysis. Neurocomputing.
2015, 149: 1415-1423. [69] 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. [70] 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. [71] 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. [72] Huali Chang, Zhenping Chen, Qinghua
Huang, Jun Shi, Xuelong Li.
Graph-based learning for segmentation of 3D ultrasound images. Neurocomputing. 2015, 151: 632-644. [73] 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. [74] 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. [75] 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. [76] 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. [77] 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. [78] 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. [79] 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. [80] 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. [81] 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. [82] 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. [83] 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. [84] 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. [85] 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] 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. [2] 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. [3] 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. [4] 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. [5] 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. [6] 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. [7] 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. [8] 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. [9] 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) [10] 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. [11] 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. [12] 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) [13] 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) [14] 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) [15] 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. [16] 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. [17] 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. [18] 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) [19] 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) [20] 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) [21] 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) [22] 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) [23] 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. [24] 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. [25] 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. [26] 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. [27] 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. [28] 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. [29] 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. [30] 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. |
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