Session Chair(s)
Muhammad Aamir, College of Computer Science and Artificial Intelligence, Huanggang Normal University, China
Uzair Aslam Bhatti, College of Information and Communication Engineering, Hainan University, Haikou, China
Nomica Choudhry, School of Information Technology, Faculty of Science, Engineering and Built Environment, Deakin University, Burwood, VIC, Australia
议题:
Medical imaging plays a vital role in modern diagnosis, prognosis, and treatment planning. With the rapid advancement of deep learning, medical image analysis has entered a transformative era, achieving significant progress in disease detection, segmentation, and classification across multiple clinical domains. Emerging architectures such as convolutional–transformer hybrids, self-supervised learning, and multimodal fusion are driving intelligent diagnostic systems with unprecedented accuracy and reliability.
This special session aims to bring together researchers, clinicians, and industry experts to share recent advances and emerging trends in deep learning for medical image interpretation and disease classification. It welcomes contributions covering diverse imaging modalities, including MRI, CT, PET, X-ray, ultrasound, retinal OCT, and histopathology, and applications in neurology, oncology, cardiology, ophthalmology, and pathology.
By fostering interdisciplinary collaboration, the session seeks to promote interpretable, robust, and clinically deployable AI systems that advance precision medicine and improve patient outcomes.
Topics of Interest (but not limited to):
• Deep learning architectures for disease detection, segmentation, and classification
• Self-supervised, semi-supervised, and weakly supervised learning in medical imaging
• Multimodal and cross-modality fusion (imaging + clinical/omics + text data)
• Federated and privacy-preserving learning for multi-institutional collaboration
• Explainable and uncertainty-aware AI for medical diagnostics
• Domain adaptation and robustness across scanners, sites, and populations
• Lightweight and real-time models for point-of-care or mobile applications
• Benchmarks, open datasets, and challenge results (e.g., BraTS, ADNI, ISLES, CheXpert, CAMELYON)
• Clinical deployment, workflow integration, and regulatory considerations
分会投稿流程:
如果您希望参加MAPM特别分会，请将您的稿件通过 ConfSync:https://confsync.cn/csae/submission系统提交，并选择 Section“Deep Learning for Medical Image Analysis and Disease Classification: Multimodal Approaches for Precision Medicine”。我们会将您的投稿分配给 Dr. Muhammad Aamir进行初审，初审通过之后将安排专家进行二审，稿件录用通知发放时间与主会议通知时间一致。有任何问题可以联系：info@confsync.cn 。