This special session focuses on artificial intelligence for medical imaging, aiming to drive intelligent transformation of clinical radiology workflows. We invite original research and practical applications on medical image segmentation and registration, computer-aided diagnosis and prognosis prediction, multimodal clinical data fusion, medical foundation models and trustworthy AI, and imaging diagnosis under limited annotations. The session emphasizes deep integration of academic research and clinical practice, and solicits technical advances in key radiologic diagnosis and treatment technologies with real-world deployment.

Topics include but are not limited to: robust segmentation/registration for radiotherapy and intervention; AI-assisted detection, diagnosis, and outcome prediction; multi-modality fusion; foundation models, self-supervised/transfer learning, uncertainty, explainability, fairness, and privacy-preserving AI; label-efficient and semi-supervised learning for rare diseases and small cohorts. We welcome studies that address real clinical challenges, report multi-center validation, clinician-AI collaboration, and translational opportunities. The session aims to connect clinicians, engineers, and industry to accelerate trustworthy AI adoption in radiology and improve patient care.

Please choose Special Session 1 to submit: https://www.zmeeting.org/submission/AAIP2027

Organizer

 

 

 

Prof. Xibin Jia, Beijing University of Technology, China

Research Interest: Computer vision, Intelligent medical image
Email: jiaxibin@bjut.edu.cn

Xibin Jia a Professor of College of Computer science at the Beijing University of Technology and Beijing Institute of Artificial Intelligence. She received her Ph.D. in Computer Application Technology from Beijing University of Technology in 2007. She was a one-year visiting scholar at University of California Riverside U.S. and a half-year visiting scholar at Flinders University Australia respectively. She has nearly 30 years of experience in computer science teaching and research. She is a Ph.D. and M.S. supervisor. Her research interests include computer vision, deep representation learning, and multi-modality deep learning. Her current work focuses on Intelligent Medical Image Analysis and Diagnosis, Affective Computing, and behavior recognition. She has served as Principal Investigator (PI) or co-PI on dozens of projects supported by grants from the National Natural Science Foundation of China, the Beijing Natural Science Foundation, and others. She has published dozens of papers in refereed journals and conference proceedings, including CVPR 2026, ACM MM 2025, MICCAI 2025 2026, IEEE-TMM. IEEE-TCSVT,IEEE JBHI, ESWA, EAAI, and IEEE/ACM-TCBB. She is a Distinguished Member of CCF and a member of several technical committees, including CCF-CV, CSIG-BVD, CSIG-MV, and CAAI-IM.

1. Chao Fan; Xibin Jia; Shaowu Xu; Hongyuan Yu; Dawei Yang; Hui Xu; Zhenghan Yang; Yan Huang and Liang Wang, Iterative Self-guided Prototype Enhancement Network for Few-Shot Medical Image Segmentation, IEEE Transactions on Multimedia, 2026
2. Xu, Shaowu ;Jia, Xibin ;Fan, Chao ;Gao, Junyu ;Chang, Jing and Sun, Qianmei. Progressive Cross-Modal Causal Intervention for Long-Term Action Recognition[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2026: 31186-31195.
3. Xu, Shaowu; Jia, Xibin; Gao, Junyu; Sun, Qianmei; Chang, Jing and Fan, Chao. Cross-Modal Dual-Causal Learning for Long-Term Action Recognition[C]//Proceedings of the 33rd ACM International Conference on Multimedia. 2025: 2919-2928.
4. Fan, Chao; Yu, Hongyuan; Huang, Yan; Wang, Liang; Yang, Zhenghan and Jia, Xibin. Slicemamba with neural architecture search for medical image segmentation[J]. IEEE Journal of Biomedical and Health Informatics, 2025, 29(10): 7446-7458
5. Fan C, Jia X, Xu S, et al. Not Just Foreground: Foreground-Background Collaborative Learning for Few-shot Medical Image Segmentation[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2026.
6. Kong X, Jia X, Yang D, et al. Concept-Enhanced Multi-Scale Cross-Modal Alignment for Medical Visual Representation Learning[J]. IEEE Journal of Biomedical and Health Informatics, 2026.
7. Fan C, Jia X, Xiao A, et al. Spenet: Self-guided prototype enhancement network for few-shot medical image segmentation[C]//International conference on medical image computing and computer-assisted intervention. Cham: Springer Nature Switzerland, 2025: 584-593.
8. Jia X, Zhang W, Xu S, et al. Artifact-Aware Prompt Learning for Domain Generalization[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer Nature Switzerland, 2026: 46-56.

Co-organizer

 

 

 

 

Assoc. Prof. Jinghua Li, Beijing University of Technology, China

Research Interest: Image processing, Machine Learning
Email: lijinghua@bjut.edu.cn

Jinghua Li an Associated Professor of School of Artificial Intelligence at the Beijing University of Technology. She received her Ph.D. in Computer Application Technology from Beijing University of Technology in 2014. She is a M.S. supervisor. Her research interests include image process, and machine learning. Her current work focuses on Intelligent Medical Image Analysis and Diagnosis, image/video analysis and understanding. She has led and participated in multiple research projects
supported by grants from the National Natural Science Foundation of China, the Beijing Natural Science Foundation, and others. She has published over 50 papers in refereed journals and conference proceedings. Currently, she serves as co-PI (Principal Investigator) on a National Natural Science Foundation of China project in collaboration with Beijing Chao-yang Hospital.

1. Jing shoran, Jinghua Li*, Dehui Kong, Baocai Yin. Dual-Branch Hypergraph Convolutional Network Learning Spectral-Spatial-Semantic Features for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing. 2026.
2. Yaping Bai, Jinghua Li*, Dehui Kong, Suqiao Yang, Tengfei Liu, Baocai Yin: LADC-Net: Lesion-aware dual-calibrated network for long-tailed medical image classification. Pattern Recognition,2026.
3. Yaping Bai, Jinghua Li, Dehui Kong, Suqiao, Yang, Baocai Yin*: EKDSC: Long-tailed recognition based on expert knowledge distillation for specific categories. Neural Networks 194: 108099 (2026)
4. Li, Jinghua and Bai, Zhuowei and Kong, Dehui and Chen, Dongpan and Li, Qianxing and Yin, Baocai*. 3d human pose estimation based on conditional dual-branch diffusion. Multimedia Systems, 2025, 31(1): 1-11.
5. Qi Zhang, Jinghua Li, Yanfeng Sun*, Shaofan Wang, Junbin Gao, Baocai Yin. Beyond Low-pass Filtering on Large-scale Graphs via Adaptive Filtering Graph Neural Networks. Neural Networks, 169(3):1-10, 2024.