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The Doctoral Consortium provides a unique opportunity for students, who are close to finishing or who have recently finished their doctorate degree, to interact with experienced researchers in digital image processing. Each Doctoral Consortium will designate a chair as mentor, and students can choose any consortium based on their preference or similarity of research interests. All students and mentors will attend a Doctoral Consortium meeting, giving the students an opportunity to discuss their ongoing research and career plans with their mentor. In addition, each student will present a poster, either describing their thesis research or a single recent paper, to the other participants and their mentors.
Students should be conducting research in digital image processing and be within one year (before or after) of graduating with their doctoral degree.
Students that meet the eligibility requirements should submit an application to firstname.lastname@example.org. You need to submit the following as a single pdf file.
1. Your CV and list of publications.
2. One first-author paper which you are most proud of.
3. The title and abstract that you will present at the consortium.
4. Three specific questions you have for your mentor.
Submission deadline: April 20, 2022
Notification of acceptance: April 30, 2022
Trends in Deep Learning based Computer
Scope and Aim: Recently, deep learning techniques like CNN and transformer have been widely applied in computer vision and achieved great success in image classification, object detection and segmentation. However, annotation cost of large amount of data has been a significant obstacle to real applications of these approaches. On the other hand, cross-modality models like CLIP (Contrastive Language-Image Pretraining) pretrained on large scale datasets are publicly available. This consortium aims to bring PhD students working is this area to share their most recent research progresses, exchange ideas and discuss possible solutions and trends toward the issues related with DL technologies. Academic experiences of the mentor will also be shared and the students could also discuss and receive possible advices for the early career development in academics.
Mentor: Prof. Linlin Shen, Shenzhen University, China
Linlin Shen is currently a Pengcheng Scholar Distinguished Professor at School of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China. He is also a Honorary professor at School of Computer Science, University of Nottingham,UK. He serves as the director of Computer Vision Institute, AI Research Center for Medical Image Analysis and Diagnosis and China-UK joint research lab for visual information processing. He also serves as the Co-Editor-in-Chief of the IET journal of Cognitive Computation and Systems. Prof. Shen received the BSc and MEng degrees from Shanghai Jiaotong University, Shanghai, China, and the Ph.D. degree from the University of Nottingham, Nottingham, U.K. He was a Research Fellow with the University of Nottingham, working on MRI brain image processing. His research interests include deep learning, facial recognition, analysis/synthesis and medical image processing. Prof. Shen is listed as the “Most Cited Chinese Researchers” by Elsevier, and listed in a ranking of the “Top 2% Scientists in the World” by Stanford University. He received the Most Cited Paper Award from the journal of Image and Vision Computing. His cell classification algorithms were the winners of the International Contest on Pattern Recognition Techniques for Indirect Immunofluorescence Images held by ICIP 2013 and ICPR 2016.
沈琳琳，教授，上海交通大学学士、硕士。英国诺丁汉大学博士。现为深圳大学计算机与软件学院“鹏城学者”特聘教授、英国诺丁汉大学计算机学院荣誉教授、深圳大学计算机视觉研究所所长、深圳大学医学影像智能分析与诊断研究中心主任、广东省教育厅中英合作视觉信息处理实验室主任，IET Cognitive Computation and Systems期刊常务副主编。他的研究方向主要为深度学习理论及其在人脸识别/分析以及医学图像分析上的应用。沈教授曾入选Elsevier中国高被引学者榜单，斯坦福大学全球前2%顶尖科学家榜单，获国际期刊《Image and Vision Computing》最多他引论文奖。他开发的细胞荧光图像分类算法连续获得IEEE ICIP 2013、ICPR 2016国际细胞图像分类算法大赛冠军。
Image/Video Deep Learning
Scope and Aim: With the development of artificial intelligence and big data processing in clinical medicine, medical image/video deep learning based on ultrasound images, endoscopy images, X-ray angiography has gradually achieved better performance in minimally invasive diagnosis and treatment than traditional medical imaging and experienced doctors. However, the challenging problem caused by low contrast image contents disturbed with the interferences of complex noises and abnormal artifacts, inaccurate and inaccessible labeled training data, heterogenous and indescribable spatiotemporal features, calls for a great breakthrough of methodology in balancing the real time, accuracy and robustness of deep learning performance. In the field of deep learning, on the one hand, model-driven deep learning such as algorithm unrolling has provided an interpretable weakly-supervised deep learning for exploiting the dual advantages of model optimization and data-driven machine learning. On the other hand, video deep learning based on convolutional neural networks and visual transformer also has paved the way for a promising methodology. The purpose of this forum is to provide a platform for doctoral students to discuss the frontier development trend of medical image/video deep learning. Through the introduction and exchange of participants' representative research work, they can learn and inspire each other and promote the transformation of application scenarios to solve practical clinical problems. The tutor will also share research experience on how to challenge clinical problems and develop independent innovation for career development after graduation.
范围和目标：随着人工智能和大数据处理在临床医学中的应用发展，基于超声图像、内窥镜图像、X-ray血管造影成像等模式的视频数据深度学习在微创诊疗中逐渐得到了比基于传统医学成像和有经验医生更好的性能。但医学图像及视频深度学习存在复杂噪声、低对比度及异常信号干扰，医生精确标注训练数据极难获取，图像时空先验信息很难表达的极大挑战，决定了医学视频图像弱监督深度学习等研究在解决实时性、准确性和鲁棒性瓶颈问题时急需寻找新的方法学突破。而在深度学习领域，一方面数据驱动深度学习结合模型优化，如优化算法深度展开(algorithm unrolling)，为深度学习结合模型优化和数据驱动的双重优势，在弱监督可解释深度学习领域开拓了很好的研究方向，另一方面基于卷积网络和基于视 觉Transformer相结合的视频深度学习也开创了极具潜力的发展思路。本该论坛目的是给博士生们提供一个医学图像/视频深度学习前沿发展趋势的讨论平台，通过各位参与者对自己代表性研究工作的介绍和交流，互相学习启发，促进应用场景转换以解决实际临床问题。导师也会分享如何挑战临床问题展开自主创新的科研经历，并为博士毕业后的人生历练和职业生涯发展分享可能的建议。
Mentor: Assoc. Prof. Binjie Qin, Shanghai Jiao Tong University, China
Binjie Qin received his M.Sc. and Ph.D. degrees from the Nanjing University of Science and Technology, Nanjing, and Shanghai Jiao Tong University, Shanghai, China, in 1999 and 2002, respectively. He was a Lecturer and an Associate Professor with the School of Life Sciences and Biotechnology, Shanghai Jiao Tong University. From 2012 to 2013, He was a Visiting Professor with the Department of Computer Science, University College London, U.K. He is currently an Associate Professor with the School of Biomedical Engineering, Shanghai Jiao Tong University. His current research interests include biomedical imaging, image processing, machine learning, computer vision, and biomedical instrumentation. He has served a reviewer for more than thirty journals including IEEE Transactions on Image Processing, IEEE Transactions on Neural Networks and Learning Systems, Medical Image Analysis, Pattern Recognition, IEEE Transactions on Cybernetics, IEEE/CAA Journal of Automatica Sinica, Bioinformatics, IEEE Transactions on Instrumentation and Measurement, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, IEEE Signal Processing Letters, Physics in Medicine and Biology. He is a leading guest editor of “Cerebral vessel extraction: from image acquisition to machine learning” in Frontiers in Neuroscience 2021-2022. He was awarded as an outstanding reviewer of Pattern Recognition 2018 and Journal of Shanghai Jiaotong University 2020, 2021. His group has developed several imaging systems for image guided ENT surgery and small animal fluorescence imaging, which have preclinical and clinical applications in some laboratories and hospitals. Prof. Qin and his group have published more than 60 papers with over 30 papers in SCI/EI indexed journals including IEEE Transactions on Image Processing, Pattern Recognition, Neural Networks, Applied Soft Computing, IEEE Transactions on Instrumentation and Measurement, Physics in Medicine and Biology, IEEE Signal Processing Letters.
秦斌杰，副教授，硕士生导师。2002年获上海交通大学工学博士学位。2002年至2004年在上海交通大学生物医学工程系任讲师，2004年至2011年任副教授。2012.08-2013.08期间任伦敦大学学院计算机系访问教授。2014.05-2015.05期间任上海交通大学科学技术发展研究院院长助理。2011年至今任上海交通大学生物医学工程学院副教授。任IEEE Transactions on Image Processing, IEEE Transactions on Neural Networks and Learning Systems, Medical Image Analysis, Pattern Recognition, IEEE Transactions on Cybernetics, IEEE/CAA Journal of Automatica Sinica, Bioinformatics, IEEE Transactions on Instrumentation and Measurement, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, IEEE Signal Processing Letters, Physics in Medicine and Biology等三十多家国际期刊的审稿人，2021年Frontiers in Neuroscience专刊"Cerebral vessel extraction: from image acquisition to machine learning"客座编辑，2021和2020年度《上海交通大学学报》有突出贡献审稿专家，2018年度Pattern Recognition杂志杰出审稿人，国家自然科学基金面上项目，教育部博士点基金项目，浙江省、黑龙江省、河北省、陕西省、广西省自然科学基金项目和省市高层次创业人才引进项目评审专家。 他主要从事生物医学成像和图像视频处理、计算机视觉和机器学习、信号处理和生命科学仪器、以及科技分类评价和创新机制等方面的研发活动。主持多项国家自然科学基金面上项目、上海市及企业合作项目，他的团队自主研发了已用于临床和科研的计算机辅助手术导航系统、生物发光和荧光成像系统等。