Computational Methods for Multimodal Medical Imaging
A Special Issue of Computation (ISSN 2079-3197) belonging to the section "Computational Biology".
Deadline for manuscript submissions: 30 January 2027 | Viewed by 230
Editors
Interests: computer vision; deep learning; pattern recognition; medical image analysis; trustworthy AI
Special Issue Information
Dear Colleagues,
The rapid advancement of medical imaging technologies has led to the widespread availability of multimodal data, including MRI, CT, PET, ultrasound, histopathology, and functional imaging. Integrating complementary information from multiple imaging modalities has become increasingly important for improving disease diagnosis, prognosis, treatment planning, and precision medicine. However, the heterogeneous nature of multimodal medical data presents significant computational and methodological challenges related to data fusion, representation learning, interpretability, scalability, and clinical generalization.
This Special Issue aims to bring together recent advances in Computational Methods for Multimodal Medical Imaging, with a particular focus on artificial intelligence, machine learning, deep learning, vision–language models, foundation models, image registration, segmentation, reconstruction, synthesis, and multimodal fusion strategies. This Special Issue will highlight novel algorithms, clinically relevant applications, and emerging frameworks that leverage complementary imaging information to improve diagnostic accuracy and decision-making in healthcare. We welcome original research articles, reviews, and methodological studies addressing topics such as multimodal image analysis, cross-modal learning, radiomics, self-supervised learning, explainable AI, federated learning, uncertainty estimation, and real-world clinical deployment.
Dr. Nasir Rahim
Dr. Muhammad Irfan
Guest Editors
Manuscript Submission Information
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Keywords
- multimodal medical imaging
- foundation models
- medical image analysis
- vision–language models
- explainable AI
- computer-aided diagnosis
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