Deep Learning and Multimodal Feature Fusion for Advanced Medical Imaging Diagnosis

Dear Colleagues,

The integration of deep learning in multimodal feature fusion is emerging as a transformative approach in medical imaging, offering promising opportunities for enhanced diagnostic accuracy, efficiency, and clinical insight. This Special Issue seeks to gather state-of-the-art research that applies artificial intelligence to advance diagnostic imaging across a wide range of clinical applications.

The focus is on innovative methods that combine imaging data such as MRI, CT, PET, and ultrasound with complementary clinical information, including electronic health records, laboratory findings, and patient history. The goal is to promote techniques that leverage the strengths of data-driven models to support early disease detection, precise characterization, and effective decision making.

Topics of interest include, but are not limited to, the following:

• Deep learning-based classification, segmentation, detection, and reconstruction;
• Multimodal feature fusion integrating image-based and non-image-based data;
• Explainable and trustworthy AI for clinical interpretation;
• Transfer learning, few-shot learning, and domain adaptation in medical imaging;
• Dataset benchmarking, reproducibility, and clinical validation studies.

We welcome original research articles, review papers, and case studies that contribute to advancing the field of AI-powered medical diagnostics through deep learning and feature fusion.

Dr. Sarmad Maqsood
Prof. Dr. Rytis Maskeliūnas
Guest Editors

Manuscript Submission Information

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