AI in MRI-based Brain Disease Prediction
Description
AI in MRI-based Brain Disease Prediction presents a comprehensive exploration of artificial intelligence technologies in the analysis of magnetic resonance imaging (MRI) for brain disease prediction. Bridging medical imaging, neuroscience, and AI, this volume covers core methodologies-such as deep learning, multimodal fusion, and fast MRI processing-and applies them to neurological disorders including Alzheimer's, Parkinson's, stroke, glioma, and autism. Featuring theoretical foundations, real-world case studies, and cutting-edge applications, the book serves as a valuable resource for researchers, clinicians, and students. It aims to foster interdisciplinary innovation and support the advancement of precision medicine in brain healthcare.
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Persons
Jin Liu is a professor at Central South University, focusing on medical image computing and AI in neuroimaging. Jianxin Wang, also a professor at Central South University, specializes in foundational AI methods and their applications in healthcare. Yi Pan is a distinguished professor at Shenzhen Institute of Advanced Technology, focusing on core AI technologies and medical applications. Together, they bring complementary expertise to promote AI-driven innovations in brain disease prediction and neuroimaging analysis.
Content
Preface. INTRODUCTION OF BRAIN AND BRAIN MRI. Brain and Magnetic Resonance Brain Imaging. Technical Foundations. AI-Empowered Fast Magnetic Resonance Imaging. MRI-BASED BRAIN DISEASE PREDICTION. Unveiling the Interdisciplinary Landscape of Brain MRI in Ophthalmology. Brain Disease Diagnosis Through AI-MRI Integration. Advancements in Intelligent Auxiliary Diagnosis for Glioma using Multimodal MRI Images. Graph-based Deep Learning for MRI-based Brain Network Analysis. AI in Stroke Segmentation Study. Multi-Scale Feature Fusion-based Sweet Spots Localization from Microelectrode Recordings in STN-DBS Surgery. Intelligent Diagnosis and Classification of Intracerebral Hemorrhage. Prediction and Diagnosis for Autism Spectrum Disorder. Multi-Structure Segmentation for STN -DBS Surgery via Contrastive Learning. Alzheimer's Disease Diagnosis Methods Based on Biomedical Data. Applications of Hypergraph Learning for Brain Disorder Diagnosis with Neuroimaging: A Survey. Index.