About this Research Topic
A variety of collaborative learning frameworks have been explored recently for better reliability of machine learning algorithms in such a complicated clinical decision-making scenario. For example, current federated learning techniques allows privacy-preserving training for models adopted by different sites with distributed storage of data (i.i.d or non-i.i.d); model compression enhances applicability of a trained model to different edge devices with different computing power; performance and robustness of independently trained models can be improved through state-of-the-arts model assembling and knowledge distillation methods; development of XAI and transfer learning are also achieving remarkable progresses for better transparency and transferability of present machine learning models; multiple techniques have also been proposed to align multi-modality or heterogeneous in latent spaces with well-defined semantic meanings. The purpose of this Research Topic is to collect original research achievements in machine learning that facilitate practical collaborative decision making for brain disease diagnosis. Any efforts made to narrow the gaps between distinct devices, data, models and different machine learning practices between sites are welcomed, but the correlation between machine learning methods and the downstream diagnostic purposes should be explicitly discussed.
We welcome articles including, but are not limited to the following topics:
• Federated learning methods for brain imaging and post-processing;
• Model compression and inference acceleration;
• Comparison between transformer models and traditional CNNs;
• 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 brain imaging data;
• Cross-modality image synthesis;
• Transparency, explainability and interpretability of deep learning models;
• Privacy and ethics issues in deep learning schemes, etc..
• Deep learning models in modern cyber physical systems
Keywords: Machine learning, Multi-modality, Federated Learning, Heterogeneous Models, Dynamic Convolution, Transformer, IoT
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.