[Call for Participation] ISICDM 2024 Sub-Forum: The Medical Large Models Forum
We cordially invite you to attend the 7th International Symposium on Image Computing and Digital Medicine (ISICDM 2024), which will be held at Shenzhen University from December 20 to 22, 2024. This year, ISICDM will feature a sub-forum on “The Medical Large Models Forum,” which will bring together top scholars from around the world to discuss the latest research and applications of large-scale models in medical imaging and healthcare.
In recent years, large models, with their exceptional capabilities in semantic understanding and generation, have emerged as key technologies in smart healthcare. These models assist medical institutions in tasks such as medical image analysis, disease progression prediction, and personalized treatment planning, providing significant support for clinical decision-making.
This forum will focus on the latest trends and challenges in the field of medical large models, showcasing original research that explores cutting-edge technologies and their applications in smart healthcare. We will also share research outcomes and practical experiences that leverage large models and AI technologies to advance smart healthcare, while delving into the challenges related to algorithm evaluation and clinical validation.
Conference Chairs
CHEN Yang
Southeast University, China
Chen Yang is a professor and PhD supervisor at the School of Computer Science and Engineering, Southeast University. He is a recipient of the National Science Fund for Distinguished Young Scholars and the principal investigator of China’s Key R&D Program funded by the Ministry of Science and Technology. He serves as the Executive Deputy Director of the Laboratory of Imaging Science and Technology and as an IEEE Senior Member. He is also the Executive Deputy Director of the Ministry of Education Key Laboratory of Computer Network and Information Integration at Southeast University, and the Chinese Co-Director of the Sino-French Biomedical Information Research Center. Additionally, he is a Deputy Director of the Medical Image Information and Control Subcommittee under the Chinese Society of Biomedical Engineering, a member of the Medical Imaging Committee of the Chinese Society of Image and Graphics, and a Standing Member of the Medical Image Committee of Jiangsu Artificial Intelligence Society.
He serves as an editor for IEEE Transactions on Computational Imaging (TCI) and BMC Medical Imaging, and as Editor-in-Chief for special issues in EURASIP Journal on Advances in Signal Processing (JASP) and International Journal of Biomedical Imaging. He is also a member of the editorial board of Intelligent Medicine and the section editor for CT Theory and Applications Research, a core Chinese journal.
CHEN Hao
Hong Kong University of Science and Technology, Hong Kong SAR, China
Chen Hao is an Assistant Professor at the Department of Computer Science and Engineering, Department of Chemical and Biological Engineering, and Division of Life Science at the Hong Kong University of Science and Technology (HKUST). He is also the Director of the HKUST Joint Center for Medical Technology, with research interests including computational pathology, multimodal data fusion, medical image analysis, and interpretable deep learning.
He has published more than 200 top-tier journal and conference papers, including contributions to MICCAI, IEEE-TMI, MIA, CVPR, ICCV, Nature Communications, Lancet Digital Health, Nature Machine Intelligence, and JAMA, with over 28,700 Google Scholar citations and an h-index of 68. He has been consecutively included in Stanford University’s list of the top 2% of global scientists and is a Clarivate Highly Cited Researcher.
Dr. Chen has received multiple awards, including the 2023 Asia Young Scientist Award, the Second Prize of the Ministry of Education National Scientific and Technology Award, the Beijing Science and Technology Progress Award (First Prize), and the MICCAI 2019 Young Scientist Impact Award. As an editor, he contributes to IEEE TMI, TNNLS, J-BHI, and CMIG, and serves as Area Chair or Program Committee Member for conferences such as ICLR, CVPR, ACM MM, and MICCAI. His team has won over 15 championships in international medical image analysis competitions.
Personal website: https://cse.hkust.edu.hk/~jhc/
Event Information
Date: December 21, 2024 (Saturday)
Time: 13:55 - 18:00
Venue: Wenhui Auditorium, Lihu Campus, Shenzhen University
Chairs: CHEN Yang (Southeast University), CHEN Hao (HKUST)
Medical Large Models Forum - Agenda
| Time | Session | Speaker | Talk Title | Chair |
|---|---|---|---|---|
| 13:55–14:00 | Opening Remarks | CHEN Yang (Southeast University) | N/A | CHEN Yang |
| 14:00–14:25 | Keynote | Mingguang He (The Hong Kong Polytechnic University) | Generative AI in Ophthalmology: From Algorithm to Clinical Solutions | CHEN Yang |
| 14:25–14:50 | Keynote | Shaohua Zhou (University of Science and Technology of China) | Generalized Foundational Models for Medical Imaging: Characteristics, Techniques, and Trends | CHEN Yang |
| 14:50–15:15 | Keynote | Yefeng Zheng (Westlake University) | Medical Large Language Models: Challenges of Hallucinations, Bias, and Automated Evaluation | CHEN Yang |
| 15:15–15:40 | Keynote | Shaoting Zhang (Shanghai Jiao Tong University, Qiyuan Research Institute) | From Fundamentals to Frontiers: Innovations and Applications of Multi-Modal Medical Models in Healthcare | CHEN Yang |
| 15:40–16:00 | Keynote | Hongbin Liu (Chinese Academy of Sciences, Hong Kong Institute of Innovation) | CARES Copilot Multi-Modal Surgical Models and Practical Applications | CHEN Yang |
| 16:00–16:25 | Tea Break | N/A | N/A | N/A |
| 16:25–16:50 | Keynote | Bin Sheng (Shanghai Jiao Tong University) | AI-Powered Diabetic Retinopathy Risk Prediction and Management System Based on Pre-Trained Models | CHEN Hao |
| 16:50–17:15 | Keynote | Weidi Xie (Shanghai Jiao Tong University) | Towards Developing Generalist Models for Healthcare | CHEN Hao |
| 17:15–17:40 | Keynote | Wenjian Qin (Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences) | Pathology Image Analysis Techniques Based on Foundational Models | CHEN Hao |
| 17:40–18:05 | Keynote | Xu Ji (Southeast University) | Task-Driven Intelligent X-Ray CT Imaging Algorithms and Applications | CHEN Hao |
Speaker Profiles and Keynote Topics
HE Mingguang
The Hong Kong Polytechnic University, Hong Kong SAR, China
Talk Title: Generative AI in Ophthalmology: From Algorithm to Clinical Solutions
Abstract: The rapid development of multimodal models and large language models has created new opportunities in ophthalmology, particularly in the clinical translation and applications of generative artificial intelligence (AI) technologies. This talk will provide an overview of research on generative AI in ophthalmology, focusing on three key aspects: image generation, large language models, and multimodal foundational models. The talk will also discuss successful achievements in areas such as cross-modality transformation, image quality enhancement, and video generation, as well as the creation of foundational models like EyeFound and EyeCLIP. In addition, it will highlight the clinical potential of these advancements in improving diagnostic accuracy and accelerating clinical translation.
Speaker Biography: Prof. Mingguang. He is the Chair Professor of Experimental Ophthalmology at The Hong Kong Polytechnic University. Prof. He undertook his medical training in China, receiving his MBBS degree from Sun Yat-sen University, Guangzhou, before going on to obtain a Master of Public Health from Johns Hopkins University in Baltimore, a MD degree in Ophthalmology from Sun Yat-sen University, and a PhD in Ophthalmology at UCL Moorfields Eye Hospital, London. Before joining PolyU, Prof. He was the former Associate Director and Professor of Ophthalmology in the Zhongshan Ophthalmic Center (ZOC), Sun Yat-sen University in Guangzhou, China. Prof. He was a NHMRC Leadership Fellow and Professor of Ophthalmic Epidemiology in The University of Melbourne and Centre for Eye Research Australia, as well as Director of the WHO Collaborating Centre for Prevention of Blindness (Australia). Prof. He founded and served as the first president of the Asia Pacific Tele-Ophthalmology Society and is a founding council member of the Asia Pacific Myopia Society and Deputy Secretary-General for the Asia Pacific Academy of Ophthalmology. Prof. He is currently the deputy Editor-in-Chief of the British Journal of Ophthalmology.
ZHOU Shaohua
University of Science and Technology of China, China
Talk Title: Generalized Foundational Models for Medical Imaging: Characteristics, Techniques, and Trends
Abstract: This talk will address the “large tasks, small data” paradox in medical imaging, introducing the training techniques for foundational models that leverage unsupervised, semi-supervised, and self-supervised learning. Key solutions, including universal task models and foundational representations, will be discussed, providing predictions for the future development of foundational model technologies.
Speaker Biography: Prof. Shaohua Zhou is a professor at the University of Science and Technology of China and the founding Executive Dean of the School of Biomedical Engineering. He is also the Director of the Medical Imaging Intelligence and Robotics (MIRACLE) Research Center. Prof. Zhou has long been committed to research innovation, application landing, and academic services in the field of medical imaging. He is a pioneer in the systematic research of “machine learning + knowledge models” in medical imaging, focusing on the challenges of “large tasks, small data.” He has authored eight academic monographs and published over 300 academic papers and chapters. In the past three years, he has published more than 40 papers in two top journals in medical image analysis. Prof. Zhou has 14 years of experience in the industry, having served as Senior R&D Director and Chief AI Expert at Siemens. He has been granted more than 150 patents, with algorithms successfully transferred to more than 10 FDA-approved products. These products have been deployed in thousands of hospitals worldwide, benefiting over 7 million patients. Prof. Zhou is a committee member and director of the Medical Image Computing and Computer-Assisted Intervention Society (MICCAI), Deputy Director of the Artificial Intelligence Branch of the Chinese Society of Biomedical Engineering, and an advisor to the Medical Open Network for AI (MONAI). He serves as an editor for top journals such as Medical Image Analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), npj Digital Medicine, and IEEE Transactions on Medical Imaging (TMI), and as a chair for top conferences such as AAAI, CVPR, ICCV, MICCAI, and NeurIPS. He has received numerous awards and recognitions for his algorithms, papers, patents, and services, including nominations for the MICCAI Young Scientist Award, ISBI Best Paper Award, RD100 Technology Oscar Award, Siemens Inventor of the Year, University of Maryland EE Outstanding Alumni Award, and BMEF Editor of the Year. Prof. Zhou is an Academic Talent of the Chinese Academy of Sciences, a Fellow of the American Institute for Medical and Biological Engineering (AIMBE), the International Academy of Medical and Biological Engineering (IAMBE), the Institute of Electrical and Electronics Engineers (IEEE), the Medical Image Computing and Computer-Assisted Intervention Society (MICCAI), and the National Academy of Inventors (NAI).
ZHENG Yefeng
Westlake University, China
Talk Title: Medical Large Language Models: Challenges of Hallucinations, Bias, and Automated Evaluation
Abstract: Recent advancements in large language models have led to significant progress in various vertical domains, including healthcare. This talk will share our work on medical foundational models. Hallucinations are a major flaw in current generative AI, particularly in the accuracy-demanding field of healthcare. We propose a contrastive decoding method to alleviate hallucinations in medical large models during medical information extraction. Bias is another flaw in current AI technologies. We evaluate the biases of mainstream large foundational models in terms of gender, age, and disease severity using over 30,000 real medical records (corresponding to 193 diseases). We propose a method to mitigate model bias by improving prompt words, thereby enhancing diagnostic accuracy. Medical large models can be applied in various medical scenarios, including pre-diagnosis, diagnosis, and post-diagnosis. Previous evaluations of large models have focused on the diagnosis stage. We propose the MedJourney evaluation platform, which includes 12 datasets (5 of which are new datasets) to comprehensively evaluate the accuracy of general large models and medical large models in assisting the entire process of medical treatment. Evaluating open medical Q&A with models is challenging. Current evaluations are based on n-gram overlap rates or large model evaluation results, which have some discrepancies with expert subjective judgments. We propose the iMap data structure, which uses large models to extract key information from answers and then matches it with key information from standard answers to obtain evaluation results that better align with expert subjective judgments.
Speaker Biography: Prof. Yefeng Zheng is a professor at Westlake University. He is also an IEEE Fellow and an AIMBE Fellow. He received his bachelor’s degree in computer science from Tsinghua University in 1998 and his master’s degree in computer science from Tsinghua University in 2001. He received his Ph.D. in electrical and computer engineering from the University of Maryland, College Park in 2005. He worked at Siemens Medical Solutions USA, Inc. from 2006 to 2017. He joined Tencent in 2018 and is currently a Tencent Distinguished Scientist and the Director of the Tianyan Laboratory. He is an Associate Editor of IEEE Transactions on Medical Imaging and has served as the Program Chair of the MICCAI 2021 conference and the Area Chair of several top AI conferences, including NeurIPS, AAAI, IJCAI, and MICCAI. He will join Westlake University full-time in July 2024 as a professor in the School of Engineering and establish the Medical AI Laboratory.
ZHANG Shaoting
Shanghai Jiao Tong University, Qiyuan Research Institute, China
Talk Title: From Fundamentals to Frontiers: Innovations and Applications of Multi-Modal Medical Models in Healthcare
Abstract: With the rapid development of artificial intelligence technology, AI foundational models have shown great potential and broad application prospects in the medical field. This report will delve into the diverse applications of foundational models in the medical field, detailing their latest technological advancements, unique advantages, and extensive application scenarios in healthcare. It will analyze their practical effectiveness and technical advantages in multiple medical scenarios such as diagnosis, treatment, and research. Through a comparative analysis of generalist and specialist models, the report will comprehensively demonstrate the specific applications and achievements of foundational models in tasks and directions such as visual recognition (medical images, etc.), natural language processing (text reports, etc.), multimodal interaction, and new drug development, revealing their applicability and limitations in different medical tasks. Additionally, the report will showcase our independently developed OpenMEDLab—a unique medical multimodal foundational model group—and discuss its practical value and future development potential in the field of smart healthcare. Furthermore, the report will provide insights and practical guidance for medical institutions, researchers, and technology developers, aiming to accelerate the technological transformation and quality improvement of the medical industry, look ahead to the future development direction of foundational models in the field of smart healthcare, and promote a new round of technological innovation in the medical field.
Speaker Biography: Prof. Shaoting Zhang is the CEO of SenseTime Medical and the Dean of the Qiyuan Research Institute at Shanghai Jiao Tong University. He received his bachelor’s, master’s, and Ph.D. degrees from Zhejiang University, Shanghai Jiao Tong University, and Rutgers University in the United States, respectively. He then served as a tenured associate professor in the Department of Computer Science at the University of North Carolina at Charlotte. His overseas research projects received funding from several sources, including the National Science Foundation (NSF), totaling millions of dollars. His paper achievements have won numerous awards, including the Young Scientist Award and Best Paper Award at top conferences in the field, as well as the Young Professor Award from the Oak Ridge Associated Universities in the United States. After returning to China, he served as the Director of the Smart Healthcare Center at the Shanghai Artificial Intelligence Laboratory. During this time, he led the development of the world’s first medical multimodal foundational model group, “PuYi,” or OpenMEDLab, which aims to provide open-source support for AI medical applications across disciplines, diseases, and modalities. He has published over 200 papers, with over 20,000 total citations and an H-index of 70. He was selected for the Shanghai Youth Science and Technology Outstanding Contribution Award and has served as the Program Committee Chair for the IPMI’25 Medical Image Analysis Conference and the CVPR’26 Computer Vision Conference.
LIU Hongbin
Chinese Academy of Sciences, Hong Kong Institute of Innovation, China
Talk Title: CARES Copilot Multi-Modal Surgical Models and Practical Applications
Abstract: The CARES Copilot 2.0 is an intelligent medical assistant developed by the AI Center of the Hong Kong Institute of Innovation, Chinese Academy of Sciences. It features four core functions: MedSearch medical search generation, MediKnow intelligent medication advice, automatic surgical report generation, and similar case retrieval. The system combines clinical needs and AI technology to improve medical efficiency and service quality. This report will provide a systematic introduction to the functions and applications of CARES Copilot 2.0.
Speaker Biography: Prof. Hongbin Liu is the Director of the AI and Robotics Innovation Center at the Hong Kong Institute of Innovation, Chinese Academy of Sciences. He is also a researcher at the Chinese Academy of Sciences and a doctoral supervisor. Prof. Liu leads the Intelligent Minimally Invasive Medical Technology Team. He is a recipient of the National High-Level Talent Program and the Chinese Academy of Sciences High-Level Talent Program. Before returning to China, he served as a lecturer, associate professor, and professor of medical robotics at King’s College London, where he also directed the Haptic Medical Robotics Laboratory. Prof. Liu has made pioneering contributions to the field of precise tactile sensing technology, minimally invasive flexible robot technology, and theoretical research related to multimodal information fusion and control. His research achievements include the world-leading ESSENCE technology for complex surface distributed tactile perception and the MicroDart system for minimally invasive flexible robots with tactile feedback. He has published over 120 papers in top international robotics journals and conferences, with an H-index of 40. He has applied for and been granted over 50 invention patents. In 2022, he was awarded the first China Construction Bank Hong Kong Innovation and Technology Award for the world’s first minimally invasive flexible brain surgery robot system.
SHENG Bin
Shanghai Jiao Tong University, China
Talk Title: AI-Powered Diabetic Retinopathy Risk Prediction and Management System Based on Pre-Trained Models
Abstract: This report discusses a deep learning system for the diagnosis and management of diabetic retinopathy (DR) based on fundus images. The system analyzes fundus images using artificial intelligence technology to accurately predict the risk and time of DR progression within the next 5 years, providing personalized DR screening interval recommendations for diabetic patients. The system includes fundus image models, clinical data models, and joint models, and innovatively proposes a disease progression analysis framework based on the Weibull mixture distribution model, treating the progression time of DR as a random variable for survival analysis and modeling. The system has been validated on large-scale datasets from multiple countries and races, demonstrating high accuracy and reliability. In addition, the system focuses on predicting retinal vascular features, revealing the pathophysiological characteristics of the subclinical stage of DR. By integrating into the clinical diagnosis and treatment process, the system achieves AI-driven stratified management, extending the average screening interval while reducing the missed diagnosis rate, improving the efficiency, fairness, and accessibility of DR screening, and providing a powerful tool for the prevention and early diagnosis of diabetic retinopathy.
Speaker Biography: Bin Sheng is a professor and doctoral supervisor in the Department of Computer Science and Engineering at Shanghai Jiao Tong University. He is also the Deputy Director of the Institute of Computer Applications and the Young Discipline Leader in the Smart Healthcare Direction of the Key Laboratory of Artificial Intelligence of the Ministry of Education. He is the Deputy Director of the International Joint Laboratory for Smart Prevention and Control of Metabolic Diseases in Shanghai. He serves as an Associate Editor for IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), The Visual Computer, and other SCI journals, as well as an Associate Editor for the Chinese VR journal Virtual Reality and Intelligent Hardware (VRIH). He is the Co-Chair of the CGI 2023, CGI 2024, and CASA 2024 conferences. Prof. Sheng has published 98 papers in SCI journals such as Nature Medicine, Nature Communications, Cell Reports Medicine, IEEE TPAMI, and IEEE TVCG, as well as over 60 papers in top conferences such as IEEE VR, ICCV, and ACM Multimedia. He has received the Computer Graphics Society Outstanding Contribution Award and the Shanghai Science and Technology Progress Special Award, and has been listed on the SAIL AWARD Honor Roll at the World Artificial Intelligence Conference twice.
XIE Weidi
Shanghai Jiao Tong University, China
Talk Title: Towards Developing Generalist Models for Healthcare
Abstract: In recent years, foundation models have achieved unprecedented success in computer vision and natural language processing. However, despite the enormous potential of the medical field, the development of foundation models in this area still lags behind. In this report, I will introduce some of our efforts and attempts in the field of medical artificial intelligence (AI4Health), including: (1) the construction of open-source datasets, such as PMC-OA, RP3D, RadMD, etc.; (2) training medical-specific language models or visual-language models, such as PMC-LLaMA, MMedLLaMA, PMC-CLIP, RadFM, etc.; and (3) enhancing the representation learning of medical knowledge and general segmentation models, such as in X-rays, pathology, or more extensive radiographic images, such as KAD, KEP, SAT. In the above work, the team aims to bridge the gap between the progress of general artificial intelligence and medical applications, providing more robust and versatile artificial intelligence solutions for the medical field. For more information, please refer to the paper here: https://weidixie.github.io/research.html
Speaker Biography: Weidi Xie is an tenure-track Associate Professor at Shanghai Jiao Tong University and a Young Scientist at the Shanghai Artificial Intelligence Laboratory. He is a National Young Talent (Overseas) and a recipient of the Shanghai (Overseas) High-Level Talent Program. Prof. Xie is the Principal Investigator of the Ministry of Science and Technology’s 2030 Science and Technology Innovation Project, “New Generation Artificial Intelligence,” and the Principal Investigator of the National Natural Science Foundation of China. He received his Ph.D. from the Visual Geometry Group (VGG) at the University of Oxford, under the supervision of Professors Andrew Zisserman and Alison Noble. He was one of the first recipients of the Google-DeepMind Full Scholarship and the China Oxford Scholarship Fund (Magdalen Award), and received the Oxford Excellence Award from the Department of Engineering at the University of Oxford. Prof. Xie’s research focuses on computer vision and medical artificial intelligence, with over 60 publications in top conferences and journals such as CVPR, ICCV, NeurIPS, ICML, IJCV, and Nature Communications. His work has been cited over 11,000 times on Google Scholar, and he has received multiple Best Paper and Best Poster Awards at top international conferences and workshops, as well as Best Journal Paper Awards. He serves as a reviewer for Nature Medicine and Nature Communications, and as an Area Chair for flagship conferences in computer vision and artificial intelligence such as CVPR, NeurIPS, and ECCV.
QIN Wenjian
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China
Talk Title: Pathology Image Analysis Techniques Based on Foundational Models
Abstract: Pathology diagnosis is recognized as the gold standard for clinical cancer diagnosis. With the rapid development of digital pathology imaging technology, it has brought revolutionary technology to the precise diagnosis and treatment of pathology. Currently, digital pathology imaging technology has been widely used in clinical diagnosis and treatment, teaching, basic research, new drug development, and remote medical care. However, existing pathology imaging and analysis technologies still face many challenges, including efficient labeling of large-scale pathology images at the cellular level and high-quality generation of virtual staining. This report will introduce the team’s preliminary exploration and practice of using foundational models in virtual staining and efficient labeling, providing new solutions for digital pathology image processing.
Speaker Biography: Wenjian Qin is a researcher at the Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences. He is a doctoral supervisor and the Director of the Joint Laboratory for Intelligent Computing Analysis of Tumor Images in Shenzhen and Hong Kong, as well as the SIAT-UAEU (United Arab Emirates University) Joint Laboratory for Intelligent Computing. He is the Chief Scientist of the National Key R&D (Youth) Program and a recipient of the Chinese Academy of Sciences Special Research and Technology Tackling Post, the Chinese Academy of Sciences Youth Promotion Association, and the Peacock Plan Special High-Level Talent. He received his Ph.D. from the University of Chinese Academy of Sciences and was jointly trained at Stanford University in the United States. His team focuses on multimodal tumor image computational imaging and intelligent analysis, developing core technologies in image reconstruction, segmentation, modeling, and visualization. They have innovatively proposed knowledge-driven cross-scale tumor image understanding theories and methods, breaking through the limitations of existing image information computing technologies in cross-scale tumor biological and spatial feature understanding and analysis. As the principal investigator, he has led over 20 projects, including the National Key R&D (Youth) Program, the National Key R&D Project, the National Natural Science Foundation of China, the Guangdong Key Area R&D, the Shenzhen Basic and Key Projects, and over 20 enterprise cooperation projects. He has published over 80 SCI/EI-indexed papers in authoritative journals and conferences such as IEEE TMI/JBHI/CI/ASE, EAAI, Radiotherapy and Oncology, and MICCAI. He has been granted 48 national invention patents and 5 software copyrights. His research achievements have been applied in clinical research on tumor surgery, radiotherapy, and immunotherapy, with 14 medical image computing technology patent products successfully transferred. He has received a second prize in the National Teaching Achievement Awards, a first prize in the Provincial Science and Technology Progress Awards, and a second prize in the Provincial Science and Technology Progress Awards.
JI Xu
Southeast University, China
Talk Title: Task-Driven Intelligent X-Ray CT Imaging Algorithms and Applications
Abstract: Due to the physical characteristics of X-ray imaging and specific clinical requirements, the original X-ray CT data may exhibit non-ideal characteristics such as beam hardening, scattering, and data incompleteness, leading to artifacts in the reconstructed CT images that affect subsequent diagnostic and therapeutic tasks. Traditional data correction methods and reconstruction algorithms cannot completely overcome these problems; in recent years, intelligent algorithms based on large model technology have been widely used in the field of medical imaging, with the potential to overcome the shortcomings of traditional imaging methods. This report will introduce the team’s research work on intelligent algorithms based on large model technology in X-ray CT imaging methods, including projection data correction, imaging algorithms for incomplete data, and spectral imaging methods, and discuss the extension of related research results to the development of domestically produced medical imaging equipment.
Speaker Biography: Xu Ji is an Associate Professor at Southeast University and a doctoral supervisor. He received his Ph.D. in Medical Physics from the University of Wisconsin-Madison in the United States and is currently an Associate Professor in the Department of Imaging Science and Technology at the School of Computer Science and Engineering, Southeast University. His research focuses on intelligent imaging methods for medical and industrial imaging systems. He has led or participated in multiple provincial and ministerial projects and has published over 20 first-author or corresponding-author SCI papers in internationally renowned journals such as IEEE Transactions on Medical Imaging and Medical Image Analysis. He has presented papers and oral presentations at major international academic conferences in the field of medical imaging, such as RSNA and SPIE Medical Imaging, and has received awards such as the Trainee Research Prize at the North American Radiology Annual Meeting. He serves as an Associate Editor for the leading medical imaging journal Medical Physics.
Registration
Participants must complete registration on the official conference website:
Registration Fees
| Registration Type | Before November 19 | November 19 to December 18 | After December 19 |
|---|---|---|---|
| Students | 1,200 RMB | 1,500 RMB | 1,800 RMB |
| Non-students | 1,600 RMB | 1,800 RMB | 2,200 RMB |
Payment Information
Transportation and accommodation are self-arranged unless otherwise provided by the conference.
Registration payment will be processed by the designated organizer: Shenzhen University Yifeng Culture Development Co., Ltd.
Account Name: Shenzhen University Yifeng Culture Development Co., Ltd.
Bank: Bank of China, Shenzhen University Branch
Account Number: 76276748927
Payment Notes
When making payments, please include the reference: Participant Name + ISICDM 2024.
For group payments, include all participant names for proper invoicing. Please upload the payment confirmation after payment.
Sponsorship and Advertising Opportunities
Sponsorship Opportunities
| Tier | Benefits | Fee |
|---|---|---|
| Diamond Sponsor | Exclusive gala dinner sponsorship and speech (10 min), 5 registrations, 6x6m booth space | 100,000 RMB |
| Gold Sponsor | 4 registrations, 4x4m booth space, promotional materials in delegate bags | 50,000 RMB |
| Silver Sponsor | 2 registrations, 3x2m booth space, promotional materials in delegate bags | 30,000 RMB |
| Bronze Sponsor | 2 registrations, company presentation opportunity | 20,000 RMB |
| Bag Sponsorship | Exclusive branding on delegate bags, 1 registration | 15,000 RMB |
| Notebook Sponsorship | Exclusive branding on notebooks, 1 registration | 8,000 RMB |
Advertising Opportunities in Conference Handbook
| Position | Dimensions | Fee |
|---|---|---|
| Inside Front Cover | 210x297mm | 5,000 RMB |
| Inside Back Cover | 210x297mm | 5,000 RMB |
| Inside Page | 210x297mm | 3,000 RMB |
| Back Cover | 210x297mm | 5,000 RMB |
Sponsorship Contact: Professor Yang, Shenzhen University
Phone: +86-13682590007
Contact Information
Website: http://www.imagecomputing.org Email: staff@isicdm.org WeChat Public Account: 图像计算与数字医学国际研讨会 (in Simplified Chinese)
We look forward to your participation!