Dear Colleagues,

In complex clinical decision-making settings, several collaborative learning frameworks have recently been investigated for enhancing the application of machine and deep learning algorithm effectiveness. For example, current federated learning techniques enable privacy-preserving model training across different sites with distributed data storage, model compression improves the applicability of a trained model to different edge devices with different computing power, and the performance and robustness of independently trained models can be improved using state-of-the-art knowledge distillation methods. The development of explainable AI and transfer learning is also making significant strides toward improving the transparency and transferability of the current machine and deep learning models. The goal of this Special Issue is to compile the most recent and advanced machine and deep learning research findings that aid in collaborative decision making for biomedical and clinical applications. Any effort to bridge the gap between various sensors, imaging devices, data, models, and machine and deep learning approaches across sites will be welcomed, but the relationship between machine and deep learning methods and downstream diagnostic objectives should be clearly stated.

We welcome articles including, but are not limited to the following topics:

• Federated learning methods for biomedical imaging and post-processing;
• Transfer learning and domain-adaptation;
• Information fusion, joint prediction using multi-modality imaging and other types of data;
• Object detection, recognition, classification, segmentation, registration and reconstruction, enhancement of biomedical imaging data;
• Cross-modality image synthesis;
• Transparency, explainability, privacy preserved and interpretability of deep learning models.

Dr. Guang Yang
Dr. Oliver Diaz
Dr. Giorgos Papanastasiou
Dr. Karim Lekadir
Guest Editors

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• AI
• machine learning
• deep learning
• federated learning
• segmentation
• detection
• image synthesis
• reconstruction
• transfer learning
• XAI