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施俊 博士，教授，博导，副院长 |
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办公室： |
tyc1286太阳集团南陈路333号翔英大楼529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，tyc1286太阳集团，通信与信息工程学院，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] 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. Accepted. [6] 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. Accepted. [7] 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. Accepted. [8] Juncheng Li, Bodong
Cheng, Ying Chen, Guangwei Gao, Jun Shi, Tieyong
Zeng. EWT: Efficient Wavelet-Transformer for single image denoising. Neural Networks. Accepted. [9] 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.
Accepted. [10] 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. 录用. [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] 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. [13] 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. [14] Lipeng Cai, Jun Shi, Shaovi Du, Yue Gao, Shihui Ying. Self-adaptive subspace representation from a
geometric intuition. Pattern
Recognition. 2024, 149: 110228. [15] 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. [16] 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. [17] 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. [18] 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. [19] 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. [20] 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. [21] 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. [22] 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. [23] 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. [24] 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. [25] 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. [26] 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. [27] 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. [28] 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. [29] 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. [30] 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. [31] 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. [32] Jinhe Dong, Jun Shi, Yue Gao, Shihui Ying.
GAME: Gaussian mixture error based meta learning architecture. Neural Computing and Applications. 2023,
35, 20445-20461. [33] 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. [34] 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. [35] 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. [36] 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. [37] 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. [38] 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. [39] 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. [40] 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. [41] 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. [42] 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. [43] 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. [44] 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. [45] 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. [46] 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. [47] 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. [48] 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. [49] 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. [50] 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. [51] 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. [52] 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. [53] 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. [54] 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. [55] 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. [56] 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. [57] 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. [58] 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. [59] 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. [60] 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. [61] 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. [62] 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. [63] 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. [64] 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. [65] 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. [66] 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. [67] 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. [68] 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. [69] 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. [70] 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. [71] 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. [72] 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. [73] 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. [74] 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. [75] 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. [76] 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. [77] 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. [78] 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. [79] 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. [80] 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. [81] 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. [82] 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. [83] 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. [84] Jun
Shi,
Qikun Jiang, Rui Mao, Minhua
Lu, Tianfu Wang. FR-KECA: fuzzy robust kernel entropy component analysis. Neurocomputing. 2015, 149: 1415-1423. [85] 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. [86] 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. [87] 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. [88] Huali Chang, Zhenping
Chen, Qinghua Huang, Jun Shi, Xuelong Li. Graph-based
learning for segmentation of 3D ultrasound images. Neurocomputing. 2015, 151: 632-644. [89] 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. [90] 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. [91] 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. [92] 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. [93] 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. [94] 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. [95] 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. [96] 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. [97] 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. [98] 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. [99] 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. [100] 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. [101] 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, early accepted. [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. |
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Jun Shi, Ph.D, Professor，Deputy Dean |
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Office： |
529
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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Email： |
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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] 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. Accepted. [5] 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. Accepted. [6] 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. Accepted. [7] Juncheng Li, Bodong
Cheng, Ying Chen, Guangwei Gao, Jun Shi, Tieyong
Zeng. EWT: Efficient Wavelet-Transformer for single image denoising. Neural Networks. Accepted. [8] 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.
Accepted. [9] 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. [10] 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. [11] 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. [12] Lipeng Cai, Jun Shi, Shaovi Du, Yue Gao, Shihui Ying. Self-adaptive subspace representation from a
geometric intuition. Pattern
Recognition. 2024, 149: 110228. [13] 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. [14] 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. [15] 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. [16] 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. [17] 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. [18] 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. [19] 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. [20] 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. [21] 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. [22] 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. [23] 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. [24] 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. [25] 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. [26] 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. [27] 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. [28] 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. [29] 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. [30] Jinhe Dong, Jun Shi, Yue Gao, Shihui Ying.
GAME: Gaussian mixture error based meta learning architecture. Neural Computing and Applications. 2023, 35:
20445-20461. [31] 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. [32] 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. [33] 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. [34] 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. [35] 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. [36] 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. [37] 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. [38] 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. [39] 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. [40] 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. [41] 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. [42] 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. [43] 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. [44] 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. [45] 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. [46] 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. [47] 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. [48] 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. [49] 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. [50] 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. [51] 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. [52] 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. [53] 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. [54] 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. [55] 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. [56] 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. [57] 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. [58] 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. [59] 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. [60] 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. [61] 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. [62] 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. [63] 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. [64] 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. [65] 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. [66] 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. [67] 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. [68] 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. [69] 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. [70] 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. [71] 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. [72] 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. [73] 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. [74] 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. [75] 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. [76] 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. [77] 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. [78] Jun
Shi,
Qikun Jiang, Rui Mao, Minhua
Lu, Tianfu Wang. FR-KECA: fuzzy robust kernel entropy component analysis. Neurocomputing. 2015, 149: 1415-1423. [79] 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. [80] 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. [81] 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. [82] Huali Chang, Zhenping
Chen, Qinghua Huang, Jun Shi, Xuelong Li. Graph-based
learning for segmentation of 3D ultrasound images. Neurocomputing. 2015, 151: 632-644. [83] 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. [84] 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. [85] 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. [86] 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. [87] 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. [88] 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. [89] 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. [90] 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. [91] 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. [92] 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. [93] 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. [94] 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. [95] 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,
early accepted. [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. |
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