Xiaodong XU, Beijing University of Posts and Telecommunications, China
Xiaodong XU is currently a tenure professor of Beijing University of Posts and Telecommunications. His research area covers mobile networking theory, semantic communication system and trial systems. He accomplished the 4G and 5G trial networks with several awards, such as the first-class S&T award of China Institute of Communications. The 6G prototype he developed in 2021 was recognized as the “industry-leading” platform. He is an expert of IMT-2030 promotion group and CIC Fellow, IEEE Senior Member.

Yimu Ji, Nanjing University of Posts and Telecommunications, China
Yimu Ji is a Professor and Ph.D. supervisor at the School of Computer Science, School of Software, and School of Cyberspace Security, Nanjing University of Posts and Telecommunications, China. He is Deputy Director of the Institute of High Performance Computing and Big Data Processing and Executive Deputy Director of the Jiangsu Engineering Research Center for High Performance Computing and Intelligent Processing. His research covers high-performance computing, cloud computing, big data, artificial intelligence, the Internet of Things, and cybersecurity. His interests include parallel algorithms, data analytics, domain-specific large models, model optimization, and security for intelligent devices and networks. He has led or participated in national and provincial research projects and has published in journals and conferences spanning intelligent computing, information processing, and security. He was a visiting scholar at the University of Virginia in 2012 and Purdue University in 2015. His work connects computing infrastructure and intelligent algorithms with applications in industry.

 

Zhi Liu, School of Information Science and Engineering, Shandong University, China
Zhi Liu (Member, IEEE) received the Ph.D. degree from the Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University in 2008. He is currently a Professor with the School of Information Science and Engineering, Shandong University. He is also the Head of the Intelligent Information Processing Group. His current research interests are intelligent computing, medical images in the neurosciences, multimodal human computer interaction, remote sensing image processing, content-based image retrieval, semantic modeling, and data mining, etc.

 

Ke Han, Institute of Forensic Science of China, China
Ke Han received the bachelor's degree in electronics and information technology from Shandong University, Jinan, China, in 2021, the master's degree in signal and information processing from Shandong University, Jinan, China, in 2024, and the doctor's degree in signal and information processing from Nanjing University of Posts and Telecommunications, Nanjing, China, in 2007. Since 2007, he has worked in Institute of Forensic Science of China, Beijing, China, and is currently a researcher with full senior professional title. His research interests concern artificial intelligence, image processing, pattern recognition, object detection, etc.

Submission Link (Enter the submission system and select Special Session 3 )

Overview:

Medical image segmentation and object detection are fundamental tasks in computer-assisted image analysis, supporting the delineation of organs, tissues, and lesions and the localization of abnormal regions. These tasks remain challenging because of ambiguous boundaries, low contrast, substantial variations in target size, limited expert annotations, and differences across imaging devices and clinical centers. This special session focuses on methods for semantic segmentation and object detection in medical images, including CT, MRI, ultrasound, and digital pathology. It welcomes research on learning with limited annotations, weakly supervised and semi-supervised methods, few-shot learning, and domain adaptation across datasets and institutions. Other topics include multimodal image fusion, anatomical and boundary priors, small-lesion detection, and joint segmentation and detection. Contributions addressing two-dimensional and three-dimensional image analysis, lightweight architectures, and computationally efficient inference are also encouraged. The session invites original algorithms, application studies, and benchmark evaluations that investigate practical challenges in medical image processing. Authors are encouraged to assess segmentation accuracy, detection sensitivity, false-positive rates, and robustness using appropriate datasets and reproducible protocols. Cross-dataset or multicenter validation is particularly welcome to examine generalization beyond a single acquisition setting. The session aims to facilitate focused discussion on accurate, efficient, and robust medical image segmentation and detection.

 

Related topics for this special session (but not limited to) :
1. Weakly supervised, semi-supervised, and few-shot semantic segmentation of medical images
2. Object detection, lesion localization, and small-lesion detection in medical images
3. Domain adaptation and generalization across imaging devices and clinical centers
4. Multimodal medical image fusion and modeling with anatomical and boundary priors
5. Lightweight methods and evaluation for 2D and 3D medical image segmentation and detection