About this Research Topic
The goal of this research topic is to gather cutting-edge research that showcases the application of deep learning methods in brain imaging for the diagnosis of neurological and psychiatric disorders. We encourage submissions that demonstrate novel approaches to overcome various abovementioned difficulties and achieve more accurate, reliable, generalizable, and interpretable diagnosis of neurological and psychiatric disorders in this field.
We welcome contributions that explore different types of deep learning architectures and their applications to brain imaging data. Some of the topics that we would like to cover in this research topic include but not limited to:
• Deep learning models for detection, classification, and prediction of neurological and psychiatric disorders based on various types of brain imaging data, such as MRI, fMRI, PET, etc
• Integration of multimodal brain imaging data using deep learning techniques
• Transfer learning and domain adaptation in brain imaging analysis
• Unsupervised/Semi-supervised training in brain imaging analysis
• Interpretability of diagnosis results for deep learning models
• Data augmentation, preprocessing, and harmonization techniques for deep learning-based brain imaging analysis
• Model compression techniques for deep models in brain imaging analysis
• Reviews and mini-reviews of recent advances highlighting future directions
Keywords: brain imaging, neuroimaging, deep learning, disease diagnosis, image-based biomarkers
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.