Secure AI for Biomedical Sensing and Imaging Applications
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".
Deadline for manuscript submissions: 31 December 2025 | Viewed by 52
Special Issue Editors
Interests: biomedical image computing; machine and deep learning; digital signal processing; privacy and security
Interests: biometrics; privacy preserving; information forensics; IoT; cybersecurity
Special Issues, Collections and Topics in MDPI journals
Interests: biometrics; security; cybersecurity; bio-cryptography
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue focuses on recent advances in secure, trustworthy, and robust AI for biomedical sensing and imaging applications. As AI technologies become increasingly embedded in biomedical systems, from diagnostic imaging to physiological signal monitoring, there is a growing need for solutions that ensure data privacy, model robustness, interpretability, and efficiency under real-world conditions. In addition to secure and privacy-preserving techniques such as adversarial defense, federated learning, and differential privacy, we also welcome research on efficient, robust, and generalizable deep learning methods that address the challenges of limited data, domain shift, and deployment on edge devices. The scope includes AI methods applied to medical imaging, wearable or implantable sensors, and other healthcare-related sensing modalities. We invite contributions that offer novel methodologies, practical frameworks, and application-driven insights. Original research articles, comprehensive reviews, and well-validated application studies are all welcome. Topics of interest include, but are not limited to, the following:
- Advanced AI for biomedical image analysis;
- Trustworthy multi-modal sensor data fusion;
- Robust AI under adversarial or noisy inputs;
- Privacy-preserving AI methods for medical diagnostics;
- Federated and distributed learning with sensitive health data;
- Explainable AI for clinical decision-making;
- Lightweight deep models for edge-based healthcare applications.
Dr. Yanming Zhu
Dr. Xuefei Yin
Prof. Dr. Jiankun Hu
Guest Editors
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