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个人简介 Personal Profile 陈宇飞，博士，副教授。2004年毕业于华东师范大学计算机科学与技术专业，获学士学位；2010年毕业于同济大学计算机应用专业，获工学博士学位；2010至2012年进入同济大学控制科学与工程博士后流动站工作；2008至2009年在德国达姆施塔特工业大学、德国弗劳恩霍夫图像数据处理研究所医学影像中心任访问研究员（Guest Researcher）；于2012年加入同济大学电信学院CAD研究中心。主要研究领域为机器视觉、机器学习、医学数据分析，具体研究方向包括医学影像分析技术、计算机辅助疾病诊断（CAD）等。与医院长期合作进行医学CAD项目研发，针对目标区域精准分割、多源影像配准与融合、肿瘤不确定性决策等问题进行研究。发表论文80余篇，其中一/二区/CCF-A/B （如TNNLS/TIP/KBS/AAAI/CVPR/MICCAI等）40余篇，授权发明专利12项；近年主持国家自然科学基金项目3项，国家重点研发计划课题1项，子课题1项，省部级项目2项；曾获上海新兴科学技术协同创新大赛优胜奖，受邀参加由上海市科委、上海市委JMRH办举办的“长三角高技术成果交易会”，对项目成果进行路演；带领团队多次获得ISICDM医学影像分析挑战赛一/二等奖，作为主要成员获上海市科学技术奖一等奖1项，三等奖1项，宁波市科学技术进步奖一等奖1项。任医学影像国际会议MICCAI-CLIP主席、图像计算与数字医学国际研讨会ISICDM挑战赛主席、医学图像计算青年研讨会MICS委员会委员等，担任领域内多个重要学术期刊及会议的审稿人。为全国仿生学标委会委员、中国图象图形学学会会员、中国体视学会智能成像分会会员、上海市计算机学会人工智能专业委员会委员等。 研究方向Research Directions 机器学习，医学影像分析 2. 机电结构优化与控制 研究内容：在对机电结构进行分析和优化的基础上，运用控制理论进行结构参数的调整，使结构性能满足设计要求。1. 仿生结构材料拓扑优化设计, 仿生机械设计 研究内容：以仿生结构为研究对象，运用连续体结构拓扑优化设计理论和方法，对多相仿生结构(机构)材料进行2. 机电结构优化与控制 研究内容：在对机电结构进行分析和优化的基础上，运用控制理论进行结构参数的调整，使结构性能满足设计要求。1. 仿生结构材料拓扑优化设计, 仿生机械设计 研究内容：以仿生结构为研究对象，运用连续体结构拓扑优化设计理论和方法，对多相仿生结构(机构)材料进行整体布局设计。 整体布局设计。 团队展示 我们是专注于人工智能在医学领域应用的团队，致力于运用AI技术解决医学难题，实现辅助医疗领域的突破与创新，探索人类健康领域的未知和可能。欢迎对此方向有兴趣、踏实认真、勤勉好学的同学加入我们，在轻松有趣、积极创新、合作互助的氛围中，共同探索前沿应用，追求更多突破！ 报考意向 招生信息 电子与信息工程学院 硕士研究生 序号 专业 招生人数 年份 1 计算机科学与技术   2024 2 计算机科学与技术   2024 博士研究生 序号 专业 招生人数 年份 1 计算机科学与技术（博士）   2024 报考意向 姓名: 手机号码： 邮箱： 毕业院校： 所学专业： 报考类型： 博士 硕士 个人简历*： 上传附件 支持扩展名：.rar .zip .doc .docx .pdf .jpg .png .jpeg 成绩单*： 上传附件 支持扩展名：.rar .zip .doc .docx .pdf .jpg .png .jpeg 其他材料： 上传附件 支持扩展名：.rar .zip .doc .docx .pdf .jpg .png .jpeg 备注： 提交 科研项目 1.    国家自然科学基金面上项目：结合证据理论与深度学习的胰腺肿瘤影像分析方法研究（编号：62173252），主持。2.    国家自然科学基金重大研究计划培育项目：多源大数据环境下胰腺肿瘤辅助诊断决策方法研究（编号：92046008），主持。3.    国家自然科学基金青年基金项目：基于领域知识的肝脏CT图割模型研究（编号：61103070），主持。4.    国家重点研发计划课题：自主软件生态系统理论模式和标准规范研究（编号：2020YFB1712301），主持。5.    国家自然科学基金项目：空间一致性约束与全局运动建模的特征匹配方法研究，排名第二。6.    上海市科技创新行动计划项目课题：设备智能化数据分析与决策支持技术研究（编号：17511103502），主持。7.    上海市科技创新行动计划项目课题：基于多源数据分析的智能检测技术研究（编号：18DZ1100704），主持。8.    上海申康临床“五新”创新研发项目（重大临床研究项目）：人工智能牙体牙髓病诊疗辅助系统的研发和临床应用，排名第二。9.    上海市卫生健康委员会科研课题：基于CBCT实现患牙三维可视化与微创开髓设计的应用研究，排名第二。10.  上海市科技创新行动计划医学创新研究专项：基于病理-影像深度学习的胰腺癌纤维化分级智能诊断研究，排名第二。11.  上海市科技创新行动计划项目：基于CT、MRI放射组学诊断胰腺癌价值及临床意义研究，排名第三。12.  同济大学青年优秀人才培养行动计划：计算机辅助肝肿瘤诊断系统关键技术研究（编号：0800219247），主持。13.  中央高校基本科研业务费-学科交叉类（重点）：沉浸式牙体牙髓病诊疗临床模拟训练系统的研发（编号：15042150012），主持。 研究成果 (1)期刊论文[1]    G. Wang, Y. Chen.MCNet: Multiscale Clustering Network for Two-view Geometry Learning and FeatureMatching, IEEE/CAA Journal of Automatica Sinica, 2023.[2]    G. Wang, H. Shi, Y. Chen,B. Wu. Unsupervised Image-to-Image Translation via Long-Short Cycle-ConsistentAdversarial Networks, Applied Intelligence, 2023.[3]    P. Yang, K. Mao, Y.Gao, Z. Wang, J. Wang, Y. Chen, C. Ma , Y. Bian, C. Shao, J. Lu. Tumor size measurements of pancreaticcancer with neoadjuvant therapy based on RECIST guidelines: is MRI as effectiveas CT?. Cancer Imaging, 2023, 23(1): 1-10.[4]    Y. Chen, C. Xu, W. Ding, S. Sun, X. Yue, H.Fujita, Target-aware U-Net with Fuzzy Skip Connections for Refined PancreasSegmentation, Applied Soft Computing, 2022, 131(109818): 1-11.[5]    J. Wang, C. Ma, P. Yang, Z.Wang, Y. Chen, Y. Bian, C. Shao, J. Lu. Diffusion Weighted Imaging of the Abdomen:Correction for Gradient Nonlinearity Bias in Apparent Diffusion Coefficient, Journal of Magnetic Resonance Imaging, 2022: 1-9.[6]    S. Xu, Y. Chen, C.Ma, X. Yue. Deep Evidential Fusion Network for Medical Image Classification,International Journal of Approximate Reasoning, 2022, 150: 188-198.[7]    W. Tan, P. Liu, X. Li, S.Xu, Y. Chen, J. Yang. Segmentation of Lung Airways Based on DeepLearning Methods, IET Image Processing, 2022: 1-13. [8]    X. Zhou, X. Yue, Z. Xu, T. Denoeux, Y. Chen.PENet: Prior Evidence Deep Neural Network for Bladder Cancer Staging, Methods,2022, 207: 20-28.[9]    X. Yue, Y. Chen, B.Yuan, Y. Lv. Three-Way Image Classification with Evidential Deep ConvolutionalNeural Networks, Cognitive Computation, 2022, 14: 2074-2086.[10]  G.Wang, Y. Chen. SCM: Spatially Coherent Matching with Gaussian FieldLearning for Nonrigid Point Set Registration, IEEE Transactions on NeuralNetworks and Learning Systems, 2021, 32(1): 203-213.[11] G. Wang, Y. Chen.Robust Feature Matching using Guided Local Outlier Factor, Pattern Recognition,2021, 117: 107986.[12] X. Lin, Y. Fu, G. Ren, X.Yang, W. Duan, Y. Chen, Q. Zhang. Micro-Computed Tomography-GuidedArtificial Intelligence for Pulp Cavity and Tooth Segmentation on Cone-beam ComputedTomography, Journal of Endodontics, 2021, 47(12): 1933-1941.[13] X. Yang, Y. Chen, X.Yue, C. Ma, P. Yang, Local Linear Embedding Based Interpolation Neural Networkin Pancreatic Tumor Segmentation, Applied Intelligence, 2021, 52(8): 8746-8756.[14] W. Duan, Y. Chen, Q.Zhang, X. Lin, X. Yang. Refined tooth and pulp segmentation using U-Net in CBCTimage, Dentomaxillofacial Radiology, 2021, 49: 20200251.[15] W. Tan, L. Zhou, X. Li, X.Yang, Y. Chen, J. Yang. Automated Vessel Segmentation in Lung CT and CTAImages via Deep Neural Networks, Journal of X-Ray Science and Technology, 2021,29(6): 1123–1137.[16]  W. Tan, P. Huang, X. Li, G. Ren, Y. Chen,J. Yang. Analysis of Segmentation of Lung Parenchyma Based on Deep LearningMethods, Journal of X-Ray Science and Technology, 2021, 29(6): 945–959.[17] X. Yue, Y. Chen, D.Miao, H. Fujita. Fuzzy Neighborhood Covering for Three-way Classification,Information Sciences, 2020, 507: 795-808.[18] X. Yue, X. Xiao, Y. Chen,J. Qian. Robust Neighborhood Covering Reduction with Determinantal PointProcess Sampling, Knowledge-Based Systems, 2020, 188: 105063.[19] H. Zheng, Y. Chen, X.Yue, C. Ma, X. Liu, P. Yang, J. Lu. Deep Pancreas Segmentation with UncertainRegions of Shadowed Sets, Magnetic Resonance Imaging, 2020, 68: 45-52.[20] X. Wu, Y. Chen, X.Liu, J. Shen, K. Zhuo, W. Zhao. Superpixel via Coarse-to-fine Boundary Shift,Applied Intelligence, 2020, 50: 2079-2092.[21] G.Wang, Y. Chen, X. Zheng. Gaussian Field Consensus: A RobustNonparametric Matching Method for Outlier Rejection. Pattern Recognition, 2018,74: 305-316.[22] X. Wu, X. Liu, Y. Chen, J. Shen, W. Zhao. A Graphbased Superpixel Generation Algorithm, Applied Intelligence, 2018, 48: 4485–4496.[23] G. Wang, Q. Zhou, Y. Chen. Robust Non-rigid Point SetRegistration Using Spatially Constrained Gaussian Fields. IEEE Transactions onImage Processing, 2017, 26(4): 1759-1769.[24] Y. Chen, X. Yue, H. Fujita, S. Fu.Three-way Decision Support for Diagnosis on Focal Liver Lesions. Knowledge-BasedSystems, 2017, 127: 85-99.[25] Y. Chen, X. Yue, R. Y. D. Xu, H. Fujita. Region Scalable ActiveContour Model with Global Constraint. Knowledge-Based Systems, 2017, 120:57-73.[26] G. Wang, Y. Chen. Fuzzy Correspondences GuidedGaussian Mixture Model for Point Set Registration, Knowledge-Based Systems,2017, 136: 200-209.[27] X. Yue, Y. Chen, D. Miao, J. Qian. Tri-partition Neighborhood CoveringReduction for Robust Classification. International Journal of ApproximateReasoning, 2017, 83: 371-384.[28] J. Hong, Y. Chen, X. Liu, W. Zhao, N. Jia, Q.Zhou. Image Structure Based Saliency Detection. Journal of Electronic Imaging,2017, 26(4): 043019.[29] Y. Ren, Y. Chen, X. Yue.Supervised Sparsity Preserving Projections for Face Recognition. Computing andInformatics, 2017, 36(4): 815-836.[30] G. Wang, Z. Wang, Y. Chen, Q. Zhou, W. Zhao. RemovingMismatches for Retinal Image Registration via Multi-Attribute-DrivenRegularized Mixture Model. Information Sciences, 2016, 372: 492-504.[31] G. Wang, Z. Wang, Y. Chen, X. Liu, Y. Ren, L. Peng.Learning Coherent Vector Fields for Robust Point Matching under Manifold Regularization.Neurocomputing, 2016, 216: 393-401.[32] Z. Wang, Y. Chen, Z. Zhu, W. Zhao. An AutomaticPanoramic Image Mosaic Method Based on Graph Model. Multimedia Tools andApplications, 2016, 75(5): 2725-2740.[33] Y. Ren, Z. Wang, Y. Chen, X. Shan, W. Zhao. Sparsity PreservingDiscriminative Learning with Applications to Face Recognition. Journal ofElectronic Imaging, 2016, 25(1): 013005.[34] G. Wang, Z. Wang, Y. Chen, W. Zhao. A Robust Non-rigid Point Set RegistrationMethod based on Asymmetric Gaussian Representation. Computer Vision andImage Understanding, 2015,141: 67-80.[35]  G. Wang, Z. Wang, Y.Chen, W. Zhao. RobustPoint Matching Method for Multimodal Retinal Image Registration. Biomedical Signal Processing and Control, 2015, 19: 68-76.[36]  Y. Chen, Z. Wang, J. Hu, W. Zhao, Q. Wu. The Domain Knowledge Based Graph-cutModel for Liver CT Segmentation, Biomedical Signal Processing and Control,2012, 7(6): 591-598.（2）会议论文[1]    W. Liu, Y. Chen, X. Yue, C. Zhang, S. Xie.Trusted Multi-View Deep Learning with Opinion Aggregation, AAAI Conference onArtificial Intelligence (AAAI), 2023. (CCF-A)[2]    X. Yang, Y. Chen, X. Yue, S. Xu, C. Ma.T-distributed Spherical Feature Representation for Imbalanced Classification,AAAI Conference on Artificial Intelligence (AAAI), 2023. (CCF-A)[3]    M. Fichmann-Levital, S. Khawaled, Y. Chen, J.A. Kennedy, and M. Freiman.Uncertainty assessment in whole-body low dose PET reconstruction usingnon-parametric Bayesian deep learning approach. Proc. IEEE 20th InternationalSymposium on Biomedical Imaging (ISBI), 2023.[4]    G. Ren, Y. Chen, S. Qi, Y. Fu, Q. Zhang.Feature Patch Based Attention Model for Dental Caries Classification. Workshopon Clinical Image-Based Procedures (MICCAI-CLIP), 2023: 62-71.[5]    W. Liu, X. Yue, Y. Chen, T. Denoeux.Trusted Multi-View Deep Learning with Opinion Aggregation, AAAI Conference onArtificial Intelligence (AAAI), 2022. (CCF-A)[6]    Q. Wu, Y. Chen, N. Huang, X. Yue. WeaklySupervised Cerebrovascular Segmentation Network with Shape Prior and ModelIndicator. ACM International Conference on Multimedia Retrieval (ICMR), 2022:668-676. (CCF-B)[7]    X. Huang, X. Yue, Z. Xu, Y. Chen.Harnessing Deep Bladder Tumor Segmentation with Logical Clinical Knowledge,International Conference on Medical Image Computing and Computer AssistedIntervention (MICCAI), 2022, 13434: 725-735. (CCF-B)[8]    X. Yang, Y. Chen, X. Yue, X. Lin, Q. Zhang.Variational Synthesis Network for Generating Micro Computed Tomography fromCone Beam Computed Tomography, IEEE International Conference on Bioinformaticsand Biomedicine (BIBM), 2021: 1611-1614. (CCF-B)[9]    X. Zhou, X. Yue, Z. Xu, T. Denoeux, Y. Chen.Deep Neural Networks with Prior Evidence for Bladder Cancer Staging, IEEEInternational Conference on Bioinformatics and Biomedicine (BIBM), 2021:1221-1226. (CCF-B)[10]  X. Huang, X. Yue, Z. Xu, Y. Chen.Integrating General and Specific Priors into Deep Convolutional Neural Networksfor Bladder Tumor Segmentation, International Joint Conference on NeuralNetworks (IJCNN), 2021: 1-8. (CCF-C)[11]  S. Xu, Y. Chen, C. Ma, X. Yue. DeepEvidential Fusion Network for Image Classification, International Conference onBelief Functions: Theory and Applications (BELIEF), 2021: 185-193.[12]  G. Wang, H. Shi, Y. Chen.Self-Augmentation with Dual-Cycle Constraint for Unsupervised Image-to-ImageGeneration, International Conference on Tools with Artificial Intelligence (ICTAI),2021: 886-890. (CCF-C)[13]  C. Zhang, X. Yue, Y. Chen,Y. Lv. Integrating Diagnosis Rules into Deep Neural Networks for Bladder CancerStaging, ACM International Conference on Information and Knowledge Management(CIKM), 2020: 2301-2304.(CCF-B)[14]  L. Luo, Y. Chen, X. Liu, Q.Deng. Feature Aware and Bilinear Feature Equal Interaction Network forClick-Through Rate Prediction, International Conference on Neural InformationProcessing (ICONIP), 2020: 432-443. (CCF-C)[15]  H. Zheng, Y. Chen, X.Yue, C. Ma. Deep Interactive Segmentation of Uncertain Regions with ShadowedSets. International Symposium on Image Computing and Digital Medicine (ISICDM),2019: 244-248.[16]  X. Chen, Y. Chen, C. Ma, X. Liu, X. Tang.Classification of Pancreatic tumors based on MRI Images using 3D ConvolutionalNeural Networks. International Symposium on Image Computing and DigitalMedicine (ISICDM), 2018:92-96.[17]  W. Xu, X. Yue, Y. Chen, M. Reformat. Ensemble ofActive Contour Based Image Segmentation. IEEE International Conference on ImageProcessing (ICIP), 2017: 86-90.(CCF-C)[18]  G. Wang, Z. Wang, Y. Chen, Q. Zhou, W. Zhao.Context-Aware Gaussian Fields for Non-rigid Point Set Registration. IEEEConference on Computer Vision and Pattern Recognition (CVPR), 2016: 5811-5819. (CCF-A)[19]  G. Wang, Z. Wang, Y. Chen, W. Zhao, X. Liu. FuzzyCorrespondences and Kernel Density Estimation for Contaminated Point Set Registration. IEEE InternationalConference on Systems, Man, and Cybernetics, 2015: 1936-1941. (CCF-C)  课程教学 本科生：    《高级语言程序设计》《高级语言程序设计（进阶）》《数据挖掘》硕士研究生：    《数据挖掘》《人工智能》博士研究生：    《智能医学基础与应用》《大数据分析算法》 学生信息 学术型硕士 郑海燕 杨小宇 徐绍勋 研究生 伍谦 付巍 金架沇 刘伟 马超 黄麒光 孙士晨 单浩炫 刘瑜琪 屈靖恩 文昕颢 周珂帆 专业学位硕士 任根强 王铭海 学生信息 当前位置：教师主页 > 学生信息 入学日期 所学专业 学号 学位 招生信息 当前位置：教师主页 > 招生信息 招生学院 招生专业 研究方向 招生人数 推免人数 考试方式 招生类别 招生年份