Data Fusion Techniques and Applications for Smart Healthcare

Editors' Preface to Data Fusion Techniques and Applications for Smart Healthcare<br>1. Retinopathy Screening from OCT Imagery via Deep Learning<br>2. Multi-sensor data fusion in digital twins for smart healthcare<br>3. Deep Learning for Multi-source Medical Information Processing<br>4. Robust watermarking algorithm based on multimodal medical image fusion<br>5. Fusion based Robust and Secure Watermarking Method for e-Healthcare Applications<br>6. Recent Advancements in Deep Learning-based Remote Photoplethysmography Methods<br>7. Federated Learning in Healthcare Applications<br>8. Riemannian Deep Feature Fusion with auto-encoders for MEG Depression Classification in Smart Healthcare applications<br>9. Epileptic Spike Localization using MEG MRI modality Fusion for Intelligent Smart Healthcare<br>10. Early classification of time series data: Overview, Challenges, and Opportunities<br>11. Deep Learning based multimodal medical image fusion<br>12. Data fusion in internet of medical things: Towards trust management, security and privacy<br>13. Feature fusion for medical data<br>14. Review on Hybrid Feature Selection and Classification of Microarray Gene Expression Data<br>15. MFFWmark: Multi focused fusion based image watermarking for telemedicine applications with BRISK feature authentication<br>16. Distributed Information Fusion for Secured Healthcare<br>17. Deep Learning for Emotion Recognition using Physiological Signals