The 2025 CSIG Youth Scientist Conference, initiated by the Youth Working Committee of the China Society of Image and Graphics, will be held from September 18-21, 2025 at the Wyndham Grand Qingdao Yingsha Beach. The conference is hosted by the China Society of Image and Graphics and co-organized by the Ocean University of China, Shandong University, and the Youth Working Committee of the China Society of Image and Graphics. Academician Yaonan Wang, Professor Houjie Wang, Professor Junyu Dong, and Professor Huimin Ma will serve as the conference chairs.

Overview

CSIG Youth Scientist Conference 2025 will be a gathering of elites and distinguished guests. 4 keynote speakers will bring us the latest insights on image and graphics, 30 thematic forums and 4 workshops will showcase the diversity of opinions in the field, and more than 200 high-level academic reports will surely spark ideas. Youth talent forums such as the Youth Talent Support Program Forum, Excellent Doctoral Dissertation Forum, and Academic Rising Star Forum will also provide a platform for young talents to speak freely, exchange ideas, and grow. At that time, 2000+ scholars and doctoral students from academia and industry in the field of image and graphics and interdisciplinary subjects will gather together to celebrate this academic event. The biomedical image analysis sub-forum, is one of the thematic forums of the conference.

Agenda

Chairs and Keynote Speakers

Chairs Information

Qing Cai is an Associate Professor and Doctoral Supervisor at the Ocean University of China. He has successively won the titles of “Taishan Scholar” Young Expert in Shandong Province, Shandong Provincial Outstanding Youth Fund, Shandong Provincial Artificial Intelligence Science and Technology Award - Outstanding Youth Award, and Ocean University of China “Youth Talent Project”. He is a Senior Member of CCF, a member of the CCF-CV Committee, a member of the CCF-MM Special Committee, a member of the CCF-AI Special Committee, a member of the CSIG Youth Working Committee, a director of the Shandong Artificial Intelligence Society, and a member of YOCSEF Qingdao. His main research direction is artificial intelligence and medical-engineering interdisciplinary direction, including medical image processing, disease-assisted diagnosis, and 3D reconstruction. Related work has been published in important academic conferences and journals at home and abroad as the first or corresponding author: CVPR, AAAI(x4), IJCAI, ACM MM, IEEE TIP(x4), IEEE TNNLS(x2), IEEE TCSVT, PR(x3), etc. He serves as a reviewer for CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML, AAAI, IJCV, IEEE TIP, IEEE TNNLS, IEEE TCYB, etc. He presides over projects such as the National Natural Science Foundation of China (General & Youth), the Shandong Provincial Natural Science Foundation (Outstanding Youth & Youth), the China Postdoctoral Science Foundation, and the Ocean University of China Youth Talent Project.

Yinghuan Shi, PhD, is a Professor and Doctoral Supervisor in the School of Computer Science and Technology at Nanjing University, Assistant Dean, and also the main person in charge of the Medical Artificial Intelligence Platform of the National Research Institute of Health and Medical Big Data at Nanjing University. She received her Bachelor’s and Doctoral degrees from the Department of Computer Science and Technology (now the School of Computer Science) at Nanjing University in 2007 and 2013, respectively. Her research interests include machine learning, pattern recognition, and their interdisciplinary research in medical image processing, AI for Science, etc. In recent years, she has presided over the National Natural Science Foundation for Excellent Young Scientists, the National Natural Science Foundation Key Project, the National Key R&D Program Digital Diagnosis Key Special Project, the National Science and Technology Innovation 2030 - New Generation Artificial Intelligence Major Project, and the Jiangsu Provincial Frontier Technology R&D Program Project. She has published more than 80 papers in CCF-A conferences and IEEE/ACM journals. She has published a popular science book “Artificial Intelligence in Your Pocket - AI and Health Care”. She has received honors such as the first Outstanding Graduate Moral Education Tutor of Nanjing University, the Wu Wenjun Artificial Intelligence Outstanding Youth Award, the China Association for Science and Technology Youth Talent Support Project, the second prize of the Jiangsu Provincial Natural Science Award (second finisher), and the Chinese People’s Liberation Army Military Medical Achievement Award (third finisher).

Xiao Jia is a Professor and Doctoral Supervisor at the School of Control Science and Engineering, Shandong University. He was selected into the National High-level Youth Talent Program, Distinguished Young and Middle-aged Scholars of Shandong University (First Level), Taishan Scholar Youth Expert of Shandong Province, and Shandong Provincial Excellent Youth Science Fund Project (Overseas), and undertakes a number of national and provincial natural science funds. He received his bachelor’s degree from the Department of Automation, Shandong University, his doctorate degree from the Department of Electronic Engineering, The Chinese University of Hong Kong, and was a postdoctoral fellow at Stanford University. Relying on the Key Laboratory of Machine Intelligence and System Control of the Ministry of Education and the Institute of Artificial Intelligence and System Control, he conducts research work. His main research directions include machine learning, multimodal intelligent perception, vision-language large models, and intelligent medical systems. He has published more than 40 papers in international academic journals and conferences such as PIEEE, EU, TASE, ICRA, IROS, and MICCAI.

Miaojing Shi is a Professor at the School of Electronics and Information Engineering, Tongji University, Vice Dean of the Scientific and Technological Research Institute, and a Visiting Professor at King’s College London. She graduated from Peking University with a Ph.D. and has served as a researcher at the French National Institute for Research in Computer Science and Automation, and as an Assistant Professor and Associate Professor in the Department of Informatics at King’s College London. Her main research interests include computer vision and medical image processing. She has published more than 90 high-level journal and conference papers. She has presided over more than 10 projects including the National Natural Science Foundation of China, the UK Engineering and Physical Sciences Research Council project, and the European Research Council project. Recently, she won the France-China Committee Personal Science and Technology Innovation Award, the King’s College London Annual Contribution Award, and the Tongji University May Fourth Youth Medal. He is a Young Thousand Talents, a Senior Member of IEEE, and a Fellow of the UK Higher Education Academy.

Daoqiang Zhang is a Professor and Dean of the School of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, and Director of the Key Laboratory of Brain-Machine Intelligence Technology of the Ministry of Education. He was selected as a national leading talent and a Fellow of the International Association for Pattern Recognition (IAPR Fellow), and was funded by the National Natural Science Foundation for Excellent Youth and Key Projects. He serves as an editor of journals such as IEEE Trans. Medical Imaging, Pattern Recognition, Machine Intelligence Research, and Intelligent Medicine, as well as the deputy editor of the journal “Data Acquisition and Processing”. He serves as a supervisor of the China Society of Image and Graphics, a director of the Alzheimer’s Disease Prevention and Treatment Association, deputy director of the Graphics Big Data Special Committee of the Chinese Society of Graphics, a standing member of the Machine Learning Special Committee of the Chinese Artificial Intelligence Society, a standing member of the Medical Information and Control Branch of the Chinese Society of Biomedical Engineering, and director of the Medical Image Processing Special Committee of the Jiangsu Artificial Intelligence Society. His main research directions are artificial intelligence, machine learning, medical image analysis, brain-computer interface, etc. He has published more than 200 academic papers, which have been cited more than 20,000 times. He has won 1 second prize of the National Natural Science Award, and 1 first prize and 1 second prize of the Ministry of Education Natural Science Award. Doctoral students/postdoctoral fellows he has supervised have won the MICCAI Young Scientist Award, an important international conference in the field of medical imaging, twice. He has been selected as an Elsevier Highly Cited Chinese Scholar for 10 consecutive years from 2014-2023.

Keynote Speakers Information

Report Title: Intelligent Medical Imaging and Processing

Abstract: The report will focuses on high-quality imaging technology based on feature learning in clinical task-driven intelligent medical imaging, the embedding of core algorithm research and development of domestic medical imaging equipment, and clinical task-driven medical image processing. It mainly talks about four parts: intelligent medical imaging, imaging algorithm application, intelligent image processing and application, and thinking on medical-engineering interdisciplinary research.

Speaker Profile: Professor Chen Yang conducts scientific research on medical imaging algorithms and intelligent image analysis, serving domestic high-end medical equipment. He has published more than 100 papers and is a Highly Cited Chinese Scholar released by Elsevier from 2022-2024. He is currently a professor at the School of Computer Science and Engineering, Southeast University, a winner of the National Science Fund for Distinguished Young Scholars, and the person in charge of the Key R&D Program of the Ministry of Science and Technology.

Report Title: Intelligent Computing for Medical Imaging - Challenges and Practices

Abstract: With the rapid development of deep learning technology, intelligent computing for medical imaging has made significant progress, but it still faces many challenges, especially how to build high-performance and reliable models in the case of scarce labeled data and long-tail distribution of diseases. In order to cope with these challenges, the application of pre-training technology in medical image analysis has gradually attracted attention. Related research is committed to using relevant or even irrelevant medical image data to pre-train the model to improve its ability to analyze various modalities of medical images. On this basis, researchers are working to build large-scale foundation models and develop model fine-tuning techniques to improve the generalization performance of models on different diagnostic tasks. This report will delve into the main challenges faced by pre-training technology in medical image analysis, including insufficient data labeling, data dimensionality issues, limitations of model capabilities, and the construction of foundation models. By sharing the research experience and insights of the research group in these fields, this report will also explore the application opportunities and challenges of pre-training and foundation models in medical image analysis, in order to provide useful references and inspirations for researchers in related fields.

Speaker Profile: Yong Xia is a Professor at the School of Computer Science/School of Artificial Intelligence, Northwestern Polytechnical University, and a member of the National Engineering Laboratory for Integrated Space-Air-Ground-Sea Big Data Application Technology. His research direction is intelligent computing for medical imaging. In the past 5 years, he has published more than 100 papers in JAMA Network Open, Radiology, IEEE-TPAMI/TMI/TIP/TNNLS, IJCV, MedIA, NeurIPS, CVPR, ECCV, MICCAI, AAAI, and IJCAI. Google citations are more than 17,000 times, H-index=61, and he has won the top three in more than 10 international subject competitions. He serves as a director of the Chinese Society for Stereology, a standing member of the Digital Medicine Branch of the China Computer Federation, etc.

Report Title: Brain Connection, Brain Network and Cognitive Ability: A Reflection Taking AD Application as an Example

Abstract: Multimodal magnetic resonance brain imaging can non-invasively provide information on the structure and functional activity of the human brain. Brain network research has opened up new avenues for understanding brain information processing mechanisms and evaluating new intervention programs. Developing precise and effective brain network computing theories and methods, and on this basis, clarifying the structural and functional organization rules, information processing patterns, and regulatory mechanisms of brain networks on cognitive functions has become a common scientific frontier for information science, brain science, etc. We will discuss with you the origin of the ideas of brain connection and brain network research based on brain imaging, the research progress of some special populations, and the progress of application in Alzheimer’s disease (AD). The focus is on reporting the recent progress of the team in selecting AD, a typical representative of neurodegenerative diseases, as the research object, from individualized brain atlases, individualized precise brain connection patterns, the development of non-invasive transcranial photobiomodulation systems, and AD intervention paradigms.

Speaker Profile: Yong Liu, PhD, Professor, Vice Dean of the School of Artificial Intelligence, Beijing University of Posts and Telecommunications. His main research direction is intelligent understanding and clinical application of brain imaging. He has published more than 50 papers as the corresponding (including co-) author, including Science Advances, Alzheimer & Dementia, eClinicalMedicine, Biological Psychiatry, Science Bulletin, etc., and has been granted 7 patents. As the project leader, he has undertaken projects including the National Natural Science Foundation Youth Fund (Category A) project, the Ministry of Science and Technology Science and Technology Innovation 2030 - Major Project, the National Key R&D Program Key Special Project, the National Natural Science Foundation Key Project, and the Beijing Natural Science Foundation for Distinguished Young Scientists (2020). He has won academic awards such as the Wu Wenjun Artificial Intelligence Science Award First Prize (2019, ranked 2nd), and has been continuously selected as an Elsevier Highly Cited Chinese Scholar (2020–).

Report Title: Pathology Large Models for Precision Cancer Diagnosis and Treatment: Challenges and Opportunities

Abstract: With the deep integration of artificial intelligence and digital pathology, pathology large models are becoming a new paradigm in the field of precision cancer diagnosis and treatment. This report will systematically explore the frontier progress of pathology large models in precision cancer diagnosis, molecular subtyping prediction, treatment response evaluation, and prognostic analysis, revealing its breakthrough potential to achieve “micro-meso-macro” association analysis through multimodal data fusion (such as whole-slide images, imaging, genomics, and clinical information). Further explore how pathology large models promote the paradigm shift of precision cancer diagnosis and treatment from “experience-driven” to “calculation-driven”.

Speaker Profile: Hao Chen, Assistant Professor, Department of Computer Science and Engineering, Department of Chemical and Biological Engineering, and Department of Life Science, Hong Kong University of Science and Technology, Director of the Medical Engineering Interdisciplinary Joint Innovation Center. His research interests include medical large models, computational pathology, multimodal fusion, medical image analysis, computer-aided minimally invasive diagnosis and treatment, etc. He has published more than 100 papers (Google Scholar citations more than 35,000 times, h-index 79) in top journals and conferences such as Nature Biomedical Engineering, Nature Communications, Lancet Digital Health, Nature Machine Intelligence, Jama, MICCAI, IEEE-TMI, MIA, CVPR, ICCV, etc. He has been continuously selected as a Stanford University global top 2% scientist and a Clarivate Analytics global highly cited scientist. He has won the 2023 Asian Young Scientist Award, the second prize of the National Ministry of Education Excellent Achievement Award, the first prize of the Beijing Science and Technology Progress Award, and the 2019 MICCAI Young Scientist Impact Award, a top conference in artificial intelligence medical imaging. He serves as an editor of journals including IEEE TMI, TNNLS, J-BHI, and CMIG, and serves as an area chair and program committee member of multiple international conferences such as ICLR, CVPR, ACM MM, and MICCAI. He has led the team to win 15 international medical image analysis challenge championships.

Report Title: Beyond The Eye: Exploring Neurodegenerative Diseases with Intelligent Analysis of Ophthalmic Images

Abstract: Due to the complexity of the brain, the ambiguity of evaluation indicators, and the lack of direct observation methods, early screening for neurodegenerative diseases such as Alzheimer’s disease (AD) is a difficult problem in the medical field. Clinically routine methods such as PET, MRI, DSA, and cognitive tests have problems such as large equipment, high cost, radiation or trauma, and long detection time, which are not suitable for popularization at the grassroots level and large-scale population screening. Therefore, exploring efficient and non-invasive screening methods has become an urgent need and research hotspot for early prevention. The eye and brain have a high degree of developmental homology and functional similarity, and the eye has the potential to become an excellent observation window for studying the brain. This report will introduce the reporter’s research work in multimodal ophthalmic medical image processing in recent years, mainly including image enhancement, structure extraction and segmentation, feature quantification, and disease diagnosis algorithm research. Focusing on the difficulties in large-scale screening of neurodegenerative diseases such as Alzheimer’s disease, this report explores the correlation between brain diseases and various eye image features, reveals the internal connections and patterns between brain diseases and retinal structural changes, and provides a scientific basis and technical support for early accurate assisted diagnosis.

Speaker Profile: Yitian Zhao is a researcher and doctoral supervisor at the Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, and the deputy director of the Advanced Diagnosis and Treatment Laboratory. He is mainly engaged in the research of artificial intelligence + ophthalmic medical image analysis. In recent years, he has focused on ophthalmic multimodal images and carried out intelligent diagnosis algorithm research and equipment development for eye, brain, heart and other related diseases. He has successively received support from the National Excellent Youth Fund, the Ministry of Science and Technology Key R&D Program Young Scientists, the Zhejiang Provincial Outstanding Youth Fund, and the China Association for Science and Technology Youth Talent Support Project, and has presided over more than ten projects including the National Natural Science Foundation. He has published more than 100 papers in top international journals and conferences in the industry such as Nature Machine Intelligence, npj Digital Medicine, IEEE PAMI, IJCV, IEEE-TMI, MedIA, CVPR, and MICCAI, with more than 9,200 academic citations and single paper highest citation times over 2300 times. He currently serves as an editor/associate editor for journals such as The Innovation (IF: 32.1) IEEE Trans. Medical Imaging (IF: 10.6) and Medical Physics (IF: 4.5).

More Information

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