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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 - 66138178 / 66137269 |
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电子邮件: |
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个人主页: |
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研究方向:(硕士生招生名额:学硕 1 名,专硕 1 名) 机器学习(深度学习)方法、医学图像(超声图像、核磁共振成像等)分析、医学信号(脑电信号、肌电信号等)处理、康复工程 教育经历: 2000.09
– 2005.06,中国科学技术大学,电子工程与信息科学系,博士 1996.09
– 2000.06,中国科学技术大学,电子工程与信息科学系,学士 工作经历: 2005.06
– 至今,上海大学,通信与信息工程学院,讲师,副教授,教授 2011.01
– 2012.01,北卡罗来纳大学教堂山分校,生物医学成像中心,访问学者 2009.07
– 2009.10,香港理工大学,医疗科技与资讯学系,访问学者 2004.07
– 2004.11,香港理工大学,赛马会复康科技中心,研究助理 2002.07
– 2003.04,香港理工大学,赛马会复康科技中心,研究助理 学术活动: MICS委员会轮值主席(2020-2021) 会议主席:2016医学影像信息处理研讨会暨第二届长三角地区医学影像分析论坛 组委会成员:2017第四届医学图像计算青年研讨会 主持项目: 国家自然科学基金面上项目、国家自然科学基金青年基金项目、国家自然科学基金重大科研仪器研制项目(合作单位主持)、国家自然科学基金重点项目(合作单位主持)、上海市自然科学基金面上项目、上海市科委项目、上海市教委项目等。 代表性期刊论文: [1] Jun Shi, Xiao
Zheng, Yan Li, Qi Zhang, Shihui Ying. Multimodal
neuroimaging feature learning with multimodal stacked deep polynomial
networks for diagnosis of Alzheimer's disease. IEEE Journal of Biomedical and Health Informatics. 2018, 22(1):
173-183. (Highly Cited Paper) [2] Feng Shi#,
Jun Wang#, Jun Shi#, Ziyan Wu, Qian Wang, Zhenyu
Tang, Kelei He, Yinghuan
Shi, Dinggang Shen*. Review of artificial
intelligence techniques in imaging data acquisition, segmentation and
diagnosis for COVID-19. IEEE Reviews in
Biomedical Engineering. 2021, 14: 4-15. (#Equal Contribution, Highly Cited Paper) [3] Zhongyi Hu,
Jun Wang, Chunxiang Zhang, Zhenzhen
Luo, Xiaoqing Luo, Lei Xiao, Jun Shi. Uncertainty modeling for
multi-center Autism Spectrum Disorder classification
using Takagi-Sugeno-Kang fuzzy systems. IEEE Transactions on Cognitive and
Developmental Systems. 2022, 14(2): 730-739. (Highly Cited Paper) [4] 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. (Highly Cited Paper) [5] 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) [6] Xiangmin Han,
Rundong Xue, Jingxi Feng,
yifan feng, Shaoyi Du, Jun
Shi*, Yue Gao*. Hypergraph foundation model for brain disease diagnosis. IEEE
Transactions on Neural Networks and Learning Systems. Accepted. [7] Tianxiang
Huang, Jing Shi, Ge Jin, Juncheng Li, Jun Wang, Qian
Wang, Jun Du*, Jun Shi*. Topological GCN guided improved conformer for
detection of Hip landmarks from ultrasound images. IEEE Journal of Biomedical and Health Informatics. Accepted. [8] Ge Jin,
Qian Zhang, Yong Cheng, Ming Xu, Yingwen Zhu, De
Yu, Yongqi Yuan, Juncheng
Li, Jun Shi*. Enhancing feature discrimination with pseudo-labels for
foundation model in segmentation of 3D medical images. Neural Networks.
Accepted. [9] Xueying
Zhou, Saisai Ding, Wenhua Zhang, Juncheng Li, Jun Wang, Jiasheng
Chen, Jun Shi *. Dual-masked contrastive learning
based hypergraph foundation model for whole slide images. Pattern
Recognition. 2026, 169: 111995. [10] Saisai
Ding, Linjin Li, Ge Jin, Jun Wang, Shihui Ying, Jun Shi*. HGMSurvNet:
A two-stage hypergraph learning network for multimodal cancer survival
prediction. Medical Image Analysis. 2025, 104: 103661. [11] Xueying
Zhou, Ge Jin, Yang Liu, Juncheng Li, Jun Wang, Shihui Ying, Yanyan Zheng, Jun
Shi*. Multi-resolution based dual-channel UNet
with cross clique for medical image dense prediction. Expert Systems With Applications. 2025,
276: 127190. [12] Jingbo He,
Bo Peng, Shihui Ying, Juncheng
Li, Yakang Dai*, Jun Shi*. Dual-domain
spatial-temporal reconstruction network for reconstruction of cine CMR. Biomedical
Signal Processing and Control. 2025, 107: 107836. [13] Juncheng Li, Hanhui Yang, Qiaosi Yi, Minhua Lu*, Jun Shi, Tieyong
Zeng*. High-frequency modulated Transformer for multi-contrast MRI
super-resolution. IEEE Transactions on Medical Imaging. 2025, 44(7):
3089-3099. [14] Hao Zhang,
Qi Wang, Jun Shi, Shihui Ying*, Zhijie Wen. Deep unfolding network with spatial alignment
for multi-modal MRI reconstruction. Medical
Image Analysis. 2025, 99: 103331. [15] Jiale Dun,
Jun Wang*, Juncheng Li, Qianhui
Yang, Wenlong Hang, Xiaofeng
Lu, Shihui Ying, Jun Shi. A trustworthy
curriculum learning guided multi-target domain adaptation network for Autism Spectrum Disorder classification. IEEE Journal of Biomedical and Health
Informatics. 2025, 29(1): 310-323. [16] Weiyi Lyu, Xinming Fang, Chaoyan Huang, Minhua Lu*, Jun
Wang, Jun Shi, Juncheng Li*. Fast
MRI reconstruction: A thorough survey from single-modal to multi-modal. Expert Systems With
Applications. 2025, 283: 127703. [17] Saisai
Ding, Juncheng Li, Jun Wang, Shihui
Ying, Jun Shi*. Multimodal co-attention fusion network with online
data augmentation for cancer subtype classification. IEEE Transactions on Medical Imaging. 2024, 43(11): 3977-3989. [18] Jian Wang,
Liang Qiao, Shichong
Zhou, Jin Zhou, Jun Wang, Juncheng Li, Shihui Ying, Cai Chang, Jun Shi*. Weakly supervised
lesion detection and diagnosis for breast cancers with partially annotated
ultrasound images. IEEE Transactions on
Medical Imaging. 2024, 43(7): 2509-2521. [19] 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. [20] Qi Wang, Zhijie Wen, Jun Shi, Qian Wang, Dinggang Shen, Shihui Ying*.
Spatial and modal optimal transport for fast cross-modal MRI reconstruction. IEEE Transactions on Medical Imaging.
2024, 43(11): 3924-3935. [21] Tianxiang
Huang, Jing Shi*, Juncheng Li, Jun Wang, Jun Du, Jun
Shi*. Involution Transformer based U-Net for landmark detection in
ultrasound images for diagnosis of infantile DDH. IEEE Journal of Biomedical and Health Informatics. 2024, 28(8):
4797-4809. [22] Ke Sun,
Jing Shi*, Ge Jin, Juncheng Li, Jun Wang, Jun Du, Jun
Shi*. Dual-domain MIM based contrastive learning for CAD of developmental
dysplasia of the hip with ultrasound images. Biomedical Signal Processing and Control. 2024, 97: 106684. [23] Huili
Zhang, Lehang Guo, Juncheng
Li, Jun Wang, Shihui Ying, Jun Shi*.
Multi-view disentanglement-based bidirectional generalized distillation for
diagnosis of liver cancers with ultrasound images. Information Processing and Management. 2024, 61: 103855. [24] Xiao Wang,
Xinping Ren, Ge Jin, Shihui
Ying, Jun Wang, Juncheng Li, Jun Shi*.
B-mode ultrasound-based CAD by learning using privileged information with
dual-level missing modality completion. Computers
in Biology and Medicine. 2024, 182: 109106. [25] 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. [26] Yan Hu,
Jun Wang*, Hao Zhu, Juncheng Li, Jun Shi.
Cost-sensitive weighted contrastive learning based on graph convolutional
networks for imbalanced Alzheimer’s disease staging. IEEE Transactions on Medical Imaging. 2024, 43(9): 3126-3136. [27] Lipeng Cai,
Jun Shi, Shaovi Du, Yue Gao, Shihui Ying*. Self-adaptive subspace representation from
a geometric intuition. Pattern
Recognition. 2024, 149: 110228. [28] Bodong
Cheng, Juncheng Li*, Jun Shi, Yingying Fang,
Guixu Zhang, Yin Chen, Tieyong
Zeng, Zhi Li*. WeaFU: Weather-informed image blind
restoration via multi-weather distribution diffusion. IEEE Transactions on Circuits and Systems for Video Technology.
2024, 34(12): 13530-13542. [29] Juncheng Li, Bodong Cheng, Ying Chen, Guangwei
Gao, Jun Shi, Tieyong Zeng. EWT: Efficient
Wavelet-Transformer for single image denoising. Neural Networks. 2024, 177: 106378. [30] Yang Zhao,
Bodong Cheng, Najun Niu, Jun Wang, Tieyong Zeng, Guixu Zhang, Jun Shi, Juncheng
Li*. Few sampling meshes-based 3D tooth segmentation
via region-aware graph convolutional network. Expert Systems With Applications. 2024,
252: 124255. [31] Juncheng Li*,
Hanhui Yang, Lok Ming Lui, Guixu
Zhang, Jun Shi, Tieyong Zeng. A lightweight
self-ensemble feedback recurrent network for Fast MRI reconstruction. International Journal of Machine Learning
and Cybernetics. 2024. [32] 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. [33] 赵阳, 李俊诚*, 成博栋, 牛娜君, 王龙光, 高广谓, 施俊. 深度学习在口腔医学影像中的应用与挑战. 中国图象图形学报.
2024, 29(3): 586-607. [34] 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. [35] 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. [36] 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. [37] 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. [38] 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. [39] 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. [40] 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. [41] 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. [42] 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. [43] 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. [44] 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. [45] 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. [46] 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. [47] 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. [48] 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. [49] 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. [50] Jinhe
Dong, Jun Shi, Yue Gao, Shihui Ying. GAME:
Gaussian mixture error based meta learning architecture. Neural Computing and Applications. 2023, 35, 20445-20461. [51] 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. [52] 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. [53] 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. [54] 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. [55] 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. [56] 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. [57] 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. [58] 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. [59] 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. [60] 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. [61] 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. [62] 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. [63] 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. [64] 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. [65] 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. [66] 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. [67] 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. [68] 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. [69] 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. [70] 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. [71] 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. [72] 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. [73] 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. [74] 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. [75] 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. [76] 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. [77] 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. [78] 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. [79] 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. [80] 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. [81] 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. [82] 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. [83] 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. [84] 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. [85] 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. [86] 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. [87] 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. [88] 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. [89] 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. [90] 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. [91] 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. [92] 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. [93] 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. [94] 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. [95] 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. [96] 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. [97] 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. [98] 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. [99] 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. [100] 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. [101] 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. [102] Jun Shi, Qikun Jiang, Rui Mao, Minhua
Lu, Tianfu Wang. FR-KECA: fuzzy robust kernel entropy component analysis. Neurocomputing. 2015, 149: 1415-1423. [103] 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. [104] 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. [105] 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. [106] Huali
Chang, Zhenping Chen, Qinghua
Huang, Jun Shi, Xuelong Li. Graph-based
learning for segmentation of 3D ultrasound images. Neurocomputing. 2015, 151: 632-644. [107] 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. [108] 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. [109] 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. [110] 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. [111] 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. [112] 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. [113] 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. [114] 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. [115] 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. [116] 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. [117] 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. [118] 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. [119] 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] Ke
Sun, Jing Shi, Jun Du, Qian Wang, Jun Shi*. Hybrid symmetry Mamba
network for ultrasound-based CAD of developmental dysplasia of the Hip. The
47th Annual International Conference of the IEEE Engineering in Medicine and
Biology Society (EMBC). 2025. [2] Tianxiang Huang,
Jing Shi, Ge Jin, Juncheng Li, Jun Wang, Jun Du*, Jun
Shi*. Topological GCN for improving detection of hip landmarks from
B-mode ultrasound Images. The 27th
International Conference on Medical Image Computing and Computer Assisted
Intervention (MICCAI). 2024 (Oral presentation, 2.7%). [3] 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. [4] 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. [5] 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. [6] 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. [7] 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. [8] 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. [9] 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. [10] 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. [11] 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) [12] 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. [13] 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. [14] 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) [15] 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) [16] 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) [17] 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. [18] 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. [19] 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. [20] 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) [21] 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) [22] 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) [23] 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) [24] 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) [25] 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) [26] 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. [27] 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. [28] 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. [29] 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. [30] 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. [31] 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. [32] 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. [33] 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. |
|
Jun Shi, Ph.D,
Professor, Deputy Dean |
|
Office: |
825
Xiangying Building, 333 Nanchen
Road, Shanghai University, Shanghai |
|
Mail Address(Zip Code): |
Box 83, 99 Shangda Road, Shanghai
University, Shanghai (200444) |
|
Phone: |
86-21-66137269/66138178 |
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Email: |
||
URL: |
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Machine Learning (Deep Learning), Medical Image (Ultrasound, MRI)
Analysis, Biomedical Signal (EEG, EEG) Processing, Rehabilitation Engineering Educational Background: 09/1996 ~ 06/2005,
Department of Electronic Engineering and Information Science, University of
Science and Technology of China (USTC) Working Experiences: 05/2005 – , School of Communication and Information Engineering,
Shanghai Institute for Advanced Communication and Data Science, Shanghai
University, Lecturer, Associate Professor, Professor 01/2011 –
01/2012, BRIC, University of North Carolina at Chapel Hill, Visiting Scholar,
Hosted by Professor Dinggang Shen 07/2009 –
10/2009, Hongkong Polytechnic University, Visiting Scholar, Hosted by
Professor Yongping Zheng 07/2004 –
11/2004, Hongkong Polytechnic University, Research Assistant 07/2002 –
04/2003, Hongkong Polytechnic University, Research Assistant Grants
and Funding: National
Natural Science Foundation of China (NSFC), Key Program of NSFC (Co-PI),
Special Fund for Basic Research on Scientific Instruments of NSFC (Co-PI),
Shanghai Municipal Natural Science Foundation, Innovation Program of Shanghai
Municipal Education Commission, etc. Selected Journal Publications: [1] Jun Shi, Xiao Zheng, Yan Li, Qi Zhang,
Shihui Ying. Multimodal neuroimaging feature
learning with multimodal stacked deep polynomial networks for diagnosis of
Alzheimer's disease. IEEE Journal of
Biomedical and Health Informatics. 2018, 22(1): 173-183. (Highly Cited Paper) [2] Feng Shi#, Jun Wang#,
Jun Shi#, Ziyan Wu, Qian Wang, Zhenyu
Tang, Kelei He, Yinghuan
Shi, Dinggang Shen*. Review of artificial
intelligence techniques in imaging data acquisition, segmentation and
diagnosis for COVID-19. IEEE Reviews in
Biomedical Engineering. 2021, 14: 4-15. (#Equal Contribution, Highly Cited Paper) [3] Zhongyi Hu, Jun Wang, Chunxiang Zhang, Zhenzhen Luo, Xiaoqing Luo, Lei Xiao, Jun Shi. Uncertainty modeling
for multi-center Autism Spectrum Disorder
classification using Takagi-Sugeno-Kang fuzzy
systems. IEEE Transactions on Cognitive
and Developmental Systems. 2022, 14(2): 730-739. (Highly Cited Paper) [4] 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. (Highly Cited Paper) [5] Xiangmin Han, Rundong Xue, Jingxi
Feng, yifan feng, Shaoyi
Du, Jun Shi*, Yue Gao*.
Hypergraph foundation model for brain disease diagnosis. IEEE Transactions
on Neural Networks and Learning Systems. Accepted. [6] Tianxiang Huang, Jing Shi, Ge Jin, Juncheng Li,
Jun Wang, Qian Wang, Jun Du*, Jun
Shi*. Topological GCN guided improved conformer for detection of Hip
landmarks from ultrasound images. IEEE
Journal of Biomedical and Health Informatics. Accepted. [7] Ge Jin, Qian Zhang, Yong Cheng,
Ming Xu, Yingwen Zhu, De Yu, Yongqi
Yuan, Juncheng Li, Jun Shi*. Enhancing feature discrimination with
pseudo-labels for foundation model in segmentation of 3D medical images. Neural
Networks. Accepted. [8] Xueying Zhou, Saisai Ding, Wenhua Zhang, Juncheng Li, Jun Wang, Jiasheng
Chen, Jun Shi *.
Dual-masked contrastive learning based hypergraph
foundation model for whole slide images. Pattern Recognition. 2026,
169: 111995. [9] Saisai Ding, Linjin Li, Ge Jin, Jun Wang, Shihui Ying, Jun
Shi*. HGMSurvNet: A two-stage hypergraph
learning network for multimodal cancer survival prediction. Medical Image
Analysis. 2025, 104: 103661. [10] Xueying Zhou, Ge Jin, Yang Liu, Juncheng Li,
Jun Wang, Shihui Ying, Yanyan
Zheng, Jun Shi*.
Multi-resolution based dual-channel UNet with cross
clique for medical image dense prediction. Expert Systems With Applications. 2025,
276: 127190. [11] Jingbo He, Bo Peng, Shihui Ying, Juncheng Li, Yakang Dai*, Jun Shi*. Dual-domain
spatial-temporal reconstruction network for reconstruction of cine CMR. Biomedical
Signal Processing and Control. 2025, 107: 107836. [12] Juncheng Li, Hanhui Yang, Qiaosi
Yi, Minhua Lu*, Jun Shi, Tieyong Zeng*.
High-frequency modulated Transformer for multi-contrast MRI super-resolution.
IEEE Transactions on Medical Imaging. 2025, 44(7): 3089-3099. [13] Hao Zhang, Qi Wang, Jun Shi, Shihui
Ying*, Zhijie Wen. Deep unfolding network with
spatial alignment for multi-modal MRI reconstruction. Medical Image Analysis. 2025, 99: 103331. [14] Jiale Dun, Jun Wang*, Juncheng Li, Qianhui Yang, Wenlong Hang, Xiaofeng Lu, Shihui Ying, Jun
Shi. A trustworthy curriculum learning guided multi-target domain
adaptation network for Autism Spectrum Disorder
classification. IEEE Journal of
Biomedical and Health Informatics. 2025, 29(1): 310-323. [15] Weiyi Lyu, Xinming Fang, Chaoyan Huang, Minhua Lu*, Jun
Wang, Jun Shi, Juncheng Li*. Fast MRI reconstruction: A thorough survey
from single-modal to multi-modal. Expert Systems With
Applications. 2025, 283: 127703. [16] Saisai Ding, Juncheng Li, Jun Wang, Shihui Ying, Jun
Shi*. Multimodal co-attention fusion network with online data
augmentation for cancer subtype classification. IEEE Transactions on Medical Imaging. 2024, 43(11): 3977-3989. [17] Jian Wang, Liang Qiao, Shichong Zhou, Jin Zhou,
Jun Wang, Juncheng Li, Shihui
Ying, Cai Chang, Jun Shi*.
Weakly supervised lesion detection and diagnosis for breast cancers with
partially annotated ultrasound images. IEEE
Transactions on Medical Imaging. 2024, 43(7): 2509-2521. [18] 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. [19] Qi Wang, Zhijie
Wen, Jun Shi, Qian Wang, Dinggang Shen, Shihui Ying*.
Spatial and modal optimal transport for fast cross-modal MRI reconstruction. IEEE Transactions on Medical Imaging. 2024, 43(11): 3924-3935. [20] Tianxiang Huang, Jing Shi*, Juncheng Li, Jun
Wang, Jun Du, Jun Shi*.
Involution Transformer based U-Net for landmark detection in ultrasound
images for diagnosis of infantile DDH. IEEE
Journal of Biomedical and Health Informatics. 2024, 28(8): 4797-4809. [21] Ke Sun, Jing Shi*, Ge Jin, Juncheng Li, Jun Wang, Jun Du, Jun Shi*. Dual-domain MIM based contrastive learning for
CAD of developmental dysplasia of the hip with ultrasound images. Biomedical Signal Processing and Control. 2024, 97: 106684. [22] Huili Zhang, Lehang Guo, Juncheng
Li, Jun Wang, Shihui Ying, Jun Shi*. Multi-view disentanglement-based bidirectional
generalized distillation for diagnosis of liver cancers with ultrasound
images. Information Processing and
Management. 2024, 61: 103855. [23] Xiao Wang, Xinping
Ren, Ge Jin, Shihui Ying, Jun Wang, Juncheng Li, Jun
Shi*. B-mode ultrasound-based CAD by learning using privileged
information with dual-level missing modality completion. Computers in Biology and Medicine. 2024, 182: 109106. [24] 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. [25] Yan Hu, Jun Wang*, Hao Zhu, Juncheng Li, Jun
Shi. Cost-sensitive weighted contrastive learning based on graph
convolutional networks for imbalanced Alzheimer’s disease staging. IEEE Transactions on Medical Imaging. 2024, 43(9): 3126-3136. [26] Lipeng Cai, Jun Shi, Shaovi Du, Yue Gao, Shihui
Ying. Self-adaptive subspace representation from a geometric intuition. Pattern Recognition. 2024, 149:
110228. [27] Bodong Cheng, Juncheng Li*, Jun Shi, Yingying Fang, Guixu Zhang, Yin Chen, Tieyong
Zeng, Zhi Li*. WeaFU: Weather-informed image blind
restoration via multi-weather distribution diffusion. IEEE Transactions on Circuits and Systems for Video Technology. 2024, 34(12): 13530-13542. [28] Juncheng Li, Bodong Cheng, Ying Chen, Guangwei Gao, Jun
Shi, Tieyong Zeng. EWT: Efficient
Wavelet-Transformer for single image denoising. Neural Networks. 2024, 177: 106378. [29] Yang Zhao, Bodong
Cheng, Najun Niu, Jun
Wang, Tieyong Zeng, Guixu
Zhang, Jun Shi, Juncheng Li*. Few sampling
meshes-based 3D tooth segmentation via region-aware graph convolutional
network. Expert Systems With Applications. 2024, 252: 124255. [30] Juncheng Li*, Hanhui Yang, Lok Ming Lui, Guixu Zhang, Jun
Shi, Tieyong Zeng. A lightweight
self-ensemble feedback recurrent network for Fast MRI reconstruction. International Journal of Machine Learning
and Cybernetics. 2024. [31] 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. [32] 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. [33] 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. [34] 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. [35] 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. [36] 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. [37] 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. [38] 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. [39] 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. [40] 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. [41] 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. [42] 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. [43] 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. [44] 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. [45] 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. [46] 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. [47] 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. [48] Jinhe Dong, Jun Shi,
Yue Gao, Shihui Ying. GAME: Gaussian mixture error
based meta learning architecture. Neural
Computing and Applications. 2023, 35:
20445-20461. [49] 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. [50] 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. [51] 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. [52] 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. [53] 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. [54] 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. [55] 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. [56] 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. [57] 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. [58] 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. [59] 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. [60] 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. [61] 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. [62] 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. [63] 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. [64] 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. [65] 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. [66] 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. [67] 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. [68] 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. [69] 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. [70] 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. [71] 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. [72] Jun Shi, Zeyu
Xue, Yakang Dai, Bo Peng, Yun Dong, Qi Zhang, Yingchun Zhang. Cascaded multi-column RVFL+ classifier
for single-modal neuroimaging-based diagnosis of Parkinson’s disease. IEEE Transactions on Biomedical
Engineering. 2019, 66(8): 2362-2371. [73] Jun Shi, Xiao Zheng, Jinjie Wu, Yan Li, Qi Zhang, Shihui
Ying. Quaternion Grassmann average network for
learning representation of histopathological image. Pattern Recognition. 2019, 89: 67-76. [74] Jun Shi, Zheng Li, Shihui
Ying, Chaofeng Wang, Qi Zhang, Pingkun
Yan. MR image super-resolution via wide residual networks with fixed skip
connection. IEEE Journal of Biomedical
and Health Informatics. 2019, 23(3): 1129-1140. [75] Yan Li, Fanqing
Meng, Jun Shi*. Learning
using privileged information improves neuroimaging-based CAD of Alzheimer's
disease: a comparative study. Medical
& Biological Engineering & Computing. 2019, 57(7): 1605-1616. [76] Xiaoyan Fei, Yun Dong, Hedi An, Qi Zhang, Yingchun Zhang, Jun
Shi*. Impact of region of interest size on transcranial sonography based computer-aided diagnosis for Parkinson’s
disease. Mathematical Biosciences and
Engineering. 2019, 16(5): 5640-5651. [77] Qi Zhang, Shuang Song, Yang
Xiao, Shuai Chen, Jun Shi, Hairong Zheng. Dual-modal artificially intelligent
diagnosis of breast tumors on both shear-wave elastography and B-mode
ultrasound using deep polynomial networks. Medical Engineering and Physics, 2019, 64: 1-6. [78] Bangming Gong, Jun Shi*,
Shihui Ying, Yakang Dai,
Qi Zhang, Yun Dong, Hedi An, Yingchun
Zhang. Neuroimaging-based diagnosis of Parkinson’s disease with deep neural
mapping large margin distribution machine. Neurocomputing. 2018, 320: 141-149. [79] Jun Shi, Qingping
Liu, Chaofeng Wang, Qi Zhang, Shihui
Ying, Haoyu Xu. Super-resolution reconstruction of
MR image with a novel residual learning network algorithm. Physics in Medicine & Biology.
2018, 63(8):085011. [80] Lehang Guo, Dan Wang, Yiyi Qian, Xiao Zheng, Chongke
Zhao, Xiaolong Li, Xiaowan
Bo, Wenwen Yue, Qi Zhang, Jun Shi*, Huixiong Xu. A
two-stage multi-view learning framework based
computer-aided diagnosis of liver tumors with contrast enhanced ultrasound
images. Clinical Hemorheology and
Microcirculation. 2018, 69(3): 343-354. [81] Shihui Ying, Zhijie Wen, Jun Shi, Yaxin Peng, Jigen Peng, Hong Qiao. Manifold
preserving: an intrinsic approach for semi-supervised distance metric
learning. IEEE Transactions on Neural
Networks and Learning Systems. 2018, 29(7): 2731-2742. [82] Qi Zhang, Yue Liu, Hong Han, Jun Shi, Wenping
Wang. Artificial intelligence based diagnosis for
cervical lymph node malignancy using the point-wise gated Boltzmann machine. IEEE Access. 2018, 6: 60605 - 60612. [83] Meihui Qiu, Huifeng
Zhang, David Mellor, Jun Shi,
Chuangxin Wu, Yueqi
Huang, Jianye Zhang, Ting Shen, Daihui
Peng. Aberrant neural activity in patients with bipolar depressive disorder
distinguishing to the unipolar depressive disorder: a resting-state
functional magnetic resonance imaging study. Frontiers in Psychiatry. 2018, 9: 238. [84] Jun Shi, Jinjie
Wu, Yan Li, Qi Zhang, Shihui Ying.
Histopathological image classification with color pattern random binary
hashing based PCANet and matrix-form classifier. IEEE Journal of Biomedical and Health
Informatics. 2017, 21(5): 1327-1337. [85] Junjie
Zhang, Jie Yin, Qi Zhang, Jun Shi*, Yan Li. Robust sound event classification with bilinear multi-column
ELM-AE and two-stage ensemble learning. EURASIP
Journal on Audio, Speech, and Music Processing. 2017, 11. [86] Huaipeng Dong, Qi Zhang, Jun
Shi. Intensity inhomogeneity compensation and tissue segmentation for
magnetic resonance imaging with noise-suppressed multiplicative intrinsic
component optimization. Optical
Engineering. 2017, 56(12): 123103. [87] Qi Zhang, Jing Yao, Yehua Cai, Limin Zhang, Yishuo Wu, Jingyu Xiong, Jun Shi, Yuanyuan Wang, Yi
Wang. Elevated hardness of peripheral gland on real-time elastography is an
independent marker for high-risk prostate cancers. La Radiologia Medica. 2017, 122(12):
944-951. [88] Qi Zhang, Yang Xiao, Jingfeng Suo, Jun
Shi, Jinhua Yu, Yi Guo, Yuanyuan Wang, Hairong Zheng. Sonoelastomics
for breast tumor classification: a radiomics approach with clustering-based
feature selection on sonoelastography. Ultrasound in Medicine and Biology.
2017, 43(5): 1058-1069. [89] Qi Zhang, Jingfeng
Suo, Wanying Chang, Jun Shi, Man Chen. Dual-modal computer-assisted evaluation
of axillary lymph node metastasis in breast cancer patients on both real-time
elastography and B-mode ultrasound. European
Journal of Radiology, 2017, 95, 66-74. [90] Qi Zhang, Congcong
Yuan, Wei Dai, Lei Tang, Jun Shi,
Zuoyong Li, Man Chen. Evaluating pathologic
response of breast cancer to neoadjuvant chemotherapy with computer-extracted
features from contrast-enhanced ultrasound videos. Physica Medica, 2017, 39, 156-163. [91] Qi Zhang, Yehua
Cai, Yinghui Hua, Jun Shi, Yuanyuan Wang, Yi Wang. Sonoelastography
shows that Achilles tendons with insertional tendinopathy are harder than
asymptomatic tendons. Knee Surgery,
Sports Traumatology, Arthroscopy. 2017, 25: 1839-1848. [92] Jun Shi, Shichong
Zhou, Xiao Liu, Qi Zhang, Minhua Lu, Tianfu Wang.
Stacked deep polynomial network based representation
learning for tumor classification with small ultrasound image dataset. Neurocomputing. 2016, 194: 87-94. [93] Qi Zhang, Yang Xiao, Wei Dai, Jingfeng Suo, Congzhi Wang, Jun Shi, Hairong
Zheng. Deep learning based classification of breast
tumors with shear-wave elastography. Ultrasonics.
2016, 72: 150-157. [94] Jun Shi, Xiao Liu, Yan Li, Qi Zhang,
Yingjie Li, Shihui Ying. Multi-channel EEG based
sleep stage classification with joint collaborative representation and
multiple kernel learning. Journal of
Neuroscience Methods. 2015, 254: 94-101. [95] Jun Shi, Qikun
Jiang, Qi Zhang, Qinghua Huang, Xuelong
Li. Sparse kernel entropy component analysis for dimensionality reduction of
biomedical data. Neurocomputing.
2015, 168: 930-940. [96] Jun Shi, Qikun
Jiang, Rui Mao, Minhua Lu, Tianfu Wang. FR-KECA:
fuzzy robust kernel entropy component analysis. Neurocomputing. 2015, 149: 1415-1423. [97] Jun Shi, Yi Li, Jie Zhu, Haojie Sun, Yin Cai. Joint sparse coding based spatial
pyramid matching for classification of color medical image. Computerized Medical Imaging and Graphics.
2015, 41: 61-66. [98] Qi Zhang, Chaolun
Li, Hong Han, Wei Dai, Jun Shi,
Yuanyuan Wang, Wenping Wang. Spatiotemporal
quantification of carotid plaque neovascularization on contrast-enhanced
ultrasound: correlation with visual grading and histopathology. European Journal of Vascular and
Endovascular Surgery. 2015, 50(3): 289-296. [99] Qi Zhang, Chaolun
Li, Moli Zhou, Yu Liao, Chunchun
Huang, Jun Shi, Yuanyuan
Wang, Wenping Wang. Quantification of carotid
plaque elasticity and intraplaque neovascularization using contrast-enhanced
ultrasound and imager egistration-based
elastography. Ultrasonics. 2015,
62: 253-262. [100] Huali Chang, Zhenping Chen, Qinghua Huang, Jun
Shi, Xuelong Li. Graph-based learning for
segmentation of 3D ultrasound images. Neurocomputing.
2015, 151: 632-644. [101] Jun Shi, Yin Cai, Jie Zhu, Jin Zhong,
Fei Wang. SEMG-based hand motion recognition using cumulative residual
entropy and extreme learning machine. Medical
& Biological Engineering & Computing. 2013, 51(4): 417-427. [102] Shichong Zhou, Jun Shi*,
Jie Zhu, Yin Cai, Ruiling Wang. Shearlet-based
texture feature extraction for classification of breast tumor in ultrasound
image. Biomedical Signal Processing and
Control. 2013, 8(6): 688-696. [103] Jun Shi, Jingyi Guo, Shuxian Hu, Yongping Zheng. Recognition of finger flexion
motion from ultrasound image: a feasibility study. Ultrasound in Medicine and Biology. 2012, 38(10): 1695-1704. [104] Jun Shi, Qian Chang, Yongping Zheng.
Feasibility of controlling a prosthetic hand using sonomyography
signal in real time: a preliminary study. Journal
of Rehabilitation Research and Development. 2010, 47(2): 87-98. [105] Jiehui Jiang, Zhuangzhi Yan, Jun Shi, et al. A mobile
monitoring system of blood pressure for underserved in China by information
and communication technology service. IEEE
Transactions on Information Technology in Biomedicine. 2010, 14(3):
748-757. [106] Xin Chen, Yongping Zheng,
Jingyi Guo, Jun Shi. Sonomyography (SMG) Control for Powered Prosthetic Hand:
A Study with Normal Subjects. Ultrasound
in Medicine and Biology. 2010, 36(7): 1076-1088. [107] Jun Shi, Yongping Zheng, Xin Chen, Hongbo Xie. Modeling the relationship between wrist angle
and muscle thickness during wrist flexion-extension based on the bone-muscle
lever system: a comparison study. Medical
Engineering and Physics. 2009, 31(10): 1125-1160. [108] Hongbo Xie, Yongping Zheng, Jingyi Guo, Xin Chen, Jun Shi. Estimation of wrist angle from sonomyography using support vector machine and artificial
neural network models. Medical
Engineering and Physics. 2009, 31(3): 384-391. [109] Jun Shi, Yongping Zheng, Qinghua Huang, Xin Chen. Continuous monitoring of sonomyography, electromyography and torque generated by
normal upper arm muscles during isometric contraction: sonomyography
assessment for arm muscles. IEEE
Transactions on Biomedical Engineering. 2008, 55(3): 1191-1198. [110] Jun Shi, Yongping Zheng, Xin Chen, et al. Assessment of muscle fatigue using sonomyography:
muscle thickness change detected from ultrasound images. Medical Engineering and Physics. 2007, 29(4): 472-479. [111] Yongping Zheng, Matthew Chan, Jun Shi, et al. Sonomyography: monitoring morphological changes of
forearm muscles in actions with the feasibility for the control of powered
prosthesis. Medical Engineering and
Physics. 2006, 28: 405-415. [112] Yongping Zheng, Jun Shi, et al. Dynamic
Depth-dependent Osmotic Swelling and Solute Diffusion in Articular Cartilage
Monitored using Real-time Ultrasound.
Ultrasound in Medicine and Biology. 2004, 30 (6): 841-849. [113] Yongping Zheng, SL Bridal, Jun Shi, et al. High resolution ultrasound elastomicroscopy
imaging of soft tissues: System development and feasibility. Physics in Medicine and Biology. 2004,
49(17): 3925-3938. Selected Conference Publications: [1] Ke
Sun, Jing Shi, Jun Du, Qian Wang, Jun
Shi*. Hybrid symmetry Mamba network for ultrasound-based CAD of
developmental dysplasia of the Hip. The 47th Annual International
Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
2025. [2] Tianxiang Huang, Jing Shi, Ge Jin, Juncheng Li,
Jun Wang, Jun Du*, Jun Shi*.
Topological GCN for improving detection of hip landmarks from B-mode
ultrasound Images. The 27th
International Conference on Medical Image Computing and Computer Assisted
Intervention (MICCAI). 2024 (Oral presentation, 2.7%). [3] 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. [4] 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. [5] 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. [6] 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. [7] 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. [8] 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. [9] 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. [10] 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. [11] 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) [12] 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. [13] 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. [14] 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) [15] 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) [16] 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) [17] 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. [18] 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. [19] 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. [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. |