About MMMDA
The management of patients with complex diseases, such as cancer, typically requires a multidisciplinary team approach, with each case discussed within a multidisciplinary team (MDT). Medical examinations often produce highly complex results, with multiple sequences of Magnetic Resonance Imaging (MRI) providing unique values for disease diagnosis. Therefore, multi-modal medical data analysis is crucial for accurate diagnosis. With the advancements in deep learning and multi-modal data analysis techniques, researchers are exploring a wide range of deep learning-based applications in medicine, including organ or lesion segmentation in medical images, disease data analysis, and more. This workshop will showcase recent advances in multi-modal medical data analysis, such as medical image processing (e.g., organ and lesion segmentation for MRI or CT), and medical text data mining. Furthermore, ideas, algorithms, and models originating from medical data are gradually being applied to other fields and have achieved positive results, such as the successful application of U-Net to scenarios like key point detection of industrial equipment. During the workshop, we will discuss key problems, common formulations, existing methodologies, real industrial applications, and future directions in these topics. Our workshop perspective combines the latest research findings with real-world requirements and experiences from the industrial community. Therefore, this workshop will inspire and benefit audiences from both academia and industry and facilitate research in multi-modal medical data for medical big data analysis modeling and extended applications.
Research Topics
- Medical related multi-modal data processing, analysis, and evaluation
- Medical image enhancement, registration, segmentation, detection, and generation
- Medical text data processing, analysis, evaluation, and interpretation
- Artificial intelligence applications in medicine, health care, sports, industry, etc.
Important URL
Important Dates
- Oct 31, 2024 (extended): Due date for full workshop papers submission
- Nov 5, 2024 (extended): Notification of paper acceptance to authors
- Nov 10, 2024: Camera-ready of accepted papers
- Dec 15-18, 2024: Workshop
Submission Instructions
Submitted papers must not substantially overlap with papers that have been published or that are simultaneously submitted to a journal or a conference with proceedings. Papers must be clearly presented in English, up to 10 pages IEEE 2-column format, including tables, figures, references and appendixes, should be submitted via the conference workshop online submission system. We also encourage the submission of short papers (4-6 pages IEEE 2-column format), or abstract papers (up to 4 page IEEE 2-column format). Papers should be formatted to IEEE Computer Society Proceedings Manuscript Formatting Guidelines (more instructions and templates can be downloaded from the conference website). All accepted papers will be included in the conference proceedings and published by the IEEE Xplore Digital Library (covered by the Engineering Index).
Formatting Instructions
IEEE Computer Society Proceedings Manuscript Formatting Guidelines
Committee Members
General Chair
Prof. Wendong Wang, School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China
Program Chair
Prof. Bo Zhang, School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China
Prof. Nanfang Xu, Peking University Health Science Center, Beijing, China
Prof. Ji Wu, Department of Electronic Engineering, Tsinghua University, Beijing, China
Prof. Xiangling Fu, School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China
Technical Program Committee
Prof. Xiuzhuang Zhou, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
Dr. Zheng Zhang, School of Modern Post, Beijing University of Posts and Telecommunications, Beijing, China
Prof. Hui Gao, School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China
Dr. Wu Liu, JD AI Research, Beijing, China
Previous information
| MMMDA 2023 | MMMDA 2022 | MMMDA 2021 |