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Machine learning for brain disorders
Organized into five parts. Part One presents the fundamentals of ML. Part Two looks at the main types of data used to characterize brain disorders, including clinical assessments, neuroimaging, electro- and magnetoencephalography, genetics and omics data, electronic health records, mobile devices, connected objects and sensors. Part Three covers the core methodologies of ML in brain disorders and the latest techniques used to study them. Part Four is dedicated to validation and datasets, and Part Five discusses applications of ML to various neurological and psychiatric disorders.
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries ; 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Virtual Event, September 27, 2021, Revised Selected Papers, Part I
The content of thebook covers the current state-of-the-art literature on federated learning applications for cancer research and Vclinical oncology analysis, as well as an overview of the deep learning approaches improving the current standard of care for brain lesions and current neuroimaging challenges. It is also focusing on the accepted BrainLes workshop submissions, is to provide an overview of new advances of medical image analysis in all the aforementioned brain pathologies. It brings together researchers from the medical image analysis domain, neurologists, and radiologists working on at least one of these diseases. The aim is to consider neuroimaging biomarkers used for one disease applied to the other diseases.
