Multimodal and Explainable AI for Biomedical Imaging and Computer-Aided Diagnosis
A Special Issue of Bioengineering (ISSN 2306-5354) belonging to the section "Biosignal Processing".
Deadline for manuscript submissions: 31 December 2026 | Viewed by 496
Editors
Interests: medical imaging AI; multimodal learning; explainable AI; computer-aided diagnosis; biomedical image analysis; foundation models in healthcare; vision transformers; AI for clinical decision support
Interests: biomedical signal and image processing; computer-aided diagnosis; pattern recognition; machine learning for medical applications
Special Issues, Collections and Topics in MDPI journals
Interests: optical imaging; machine learning; biomedicine; bioengineering; biophotonics; translational research; interdisciplinary
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapid emergence of foundation models, transformers, and multimodal AI systems is redefining computer-aided diagnosis and biomedical imaging. While traditional machine learning and deep learning approaches have already transformed medical image analysis, the new generation of AI systems—large language models (LLMs), vision–language models (VLMs), self-supervised learning frameworks, and multimodal transformers—is enabling more generalizable, data-efficient, and clinically adaptable diagnostic tools.
These advances open new possibilities for integrating heterogeneous medical data sources, including imaging, clinical records, signals, and text, while also raising crucial challenges related to explainability, robustness, and trust in clinical environments. This Special Issue aims to gather cutting-edge research bridging methodological AI innovations and real-world healthcare applications. We particularly welcome contributions on foundation models in medical imaging, multimodal learning, self-supervised and weakly supervised approaches, explainable and trustworthy AI, federated learning, and privacy-preserving diagnostic systems.
By highlighting emerging paradigms beyond conventional machine learning, this issue seeks to shape the next generation of AI-enabled computer-aided diagnostic technologies.
Dr. Nesma Settouti
Prof. Dr. Mouloud Adel
Dr. Daniel L. Farkas
Guest Editors
Manuscript Submission Information
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Keywords
- foundation models
- transformers
- multimodal AI
- medical imaging
- computer-aided diagnosis
- biomedical image analysis
- vision transformers
- large language models
- vision-language models
- self-supervised learning
- weakly supervised learning
- explainable AI
- trustworthy AI
- federated learning
- clinical decision support
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