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陈宇飞
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副教授 硕,博士生导师
副教授 硕,博士生导师
计算机科学与技术学院(软件学院)
计算机科学与技术  、 电子信息
yufeichen@tongji.edu.cn
021-69587942

个人简介

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

机器学习,医学影像分析
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!

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博士研究生
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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 Feature Matching, 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-Consistent Adversarial 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 pancreatic cancer with neoadjuvant therapy based on RECIST guidelines: is MRI as effective as 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 Pancreas Segmentation, 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 Deep Learning 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 Convolutional Neural Networks, Cognitive Computation, 2022, 14: 2074-2086.

[10]  G. Wang, Y. Chen. SCM: Spatially Coherent Matching with Gaussian Field Learning for Nonrigid Point Set Registration, IEEE Transactions on Neural Networks 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-Guided Artificial Intelligence for Pulp Cavity and Tooth Segmentation on Cone-beam Computed Tomography, 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 Network in 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 CBCT image, 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 CTA Images 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 Learning Methods, 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 Point Process 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 Uncertain Regions 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 Robust Nonparametric Matching Method for Outlier Rejection. Pattern Recognition, 2018, 74: 305-316.

[22]  X. Wu, X. Liu, Y. Chen, J. Shen, W. Zhao. A Graph based Superpixel Generation Algorithm, Applied Intelligence, 2018, 48: 4485–4496.

[23]  G. Wang, Q. Zhou, Y. Chen. Robust Non-rigid Point Set Registration Using Spatially Constrained Gaussian Fields. IEEE Transactions on Image 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-Based Systems, 2017, 127: 85-99.

[25]  Y. Chen, X. Yue, R. Y. D. Xu, H. Fujita. Region Scalable Active Contour Model with Global Constraint. Knowledge-Based Systems, 2017, 120: 57-73.

[26]  G. Wang, Y. Chen. Fuzzy Correspondences Guided Gaussian 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 Covering Reduction for Robust Classification. International Journal of Approximate Reasoning, 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 and Informatics, 2017, 36(4): 815-836.

[30]  G. Wang, Z. Wang, Y. Chen, Q. Zhou, W. Zhao. Removing Mismatches for Retinal Image Registration via Multi-Attribute-Driven Regularized 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 Automatic Panoramic Image Mosaic Method Based on Graph Model. Multimedia Tools and Applications, 2016, 75(5): 2725-2740.

[33]  Y. Ren, Z. Wang, Y. Chen, X. Shan, W. Zhao. Sparsity Preserving Discriminative Learning with Applications to Face Recognition. Journal of Electronic Imaging, 2016, 25(1): 013005.

[34]  G. Wang, Z. Wang, Y. Chen, W. Zhao. A Robust Non-rigid Point Set Registration Method based on Asymmetric Gaussian Representation. Computer Vision and Image Understanding, 2015, 141: 67-80.

[35]  G. Wang, Z. Wang, Y. Chen, W. Zhao. Robust Point 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-cut Model 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 on Artificial 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 using non-parametric Bayesian deep learning approach. Proc. IEEE 20th International Symposium 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. Workshop on 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 on Artificial Intelligence (AAAI), 2022. (CCF-A)

[6]     Q. Wu, Y. Chen, N. Huang, X. Yue. Weakly Supervised Cerebrovascular Segmentation Network with Shape Prior and Model Indicator. 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 Assisted Intervention (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 from Cone Beam Computed Tomography, IEEE International Conference on Bioinformatics and 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, IEEE International 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 Networks for Bladder Tumor Segmentation, International Joint Conference on Neural Networks (IJCNN), 2021: 1-8. (CCF-C)

[11]   S. Xu, Y. Chen, C. Ma, X. Yue. Deep Evidential Fusion Network for Image Classification, International Conference on Belief 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-Image Generation, 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 Cancer Staging, 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 for Click-Through Rate Prediction, International Conference on Neural Information Processing (ICONIP), 2020: 432-443. (CCF-C)

[15]   H. Zheng, Y. Chen, X. Yue, C. Ma. Deep Interactive Segmentation of Uncertain Regions with Shadowed Sets. 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 Convolutional Neural Networks. International Symposium on Image Computing and Digital Medicine (ISICDM), 2018:92-96.

[17]   W. Xu, X. Yue, Y. Chen, M. Reformat. Ensemble of Active Contour Based Image Segmentation. IEEE International Conference on Image Processing (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. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016: 5811-5819. (CCF-A)

[19]   G. Wang, Z. Wang, Y. Chen, W. Zhao, X. Liu. Fuzzy Correspondences and Kernel Density Estimation for Contaminated Point Set Registration. IEEE International Conference 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》


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