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
Aim and Scope:
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
Submission Process:
If you wish to participate in this special session--MAPM, please submit your manuscript through the ConfSync:https://confsync.cn/csae/submission and select the Section "Deep Learning for Medical Image Analysis and Disease Classification: Multimodal Approaches for Precision Medicine".We will assign your submission to Dr. Muhammad Aamir for a preliminary review. After passing the preliminary review, your manuscript will undergo a secondary review by experts. Notifications of acceptance will be issued concurrently with the main conference notifications. For any questions, please contact: info@confsync.cn.