Connectomics in NeuroImaging, Markus D. Schirmer; Archana Venkataraman; Islem Re
Автор: Rish Название: Machine Learning and Interpretation in Neuroimaging ISBN: 3319451731 ISBN-13(EAN): 9783319451732 Издательство: Springer Рейтинг: Цена: 5870.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Networks and Decoding.- Multi-Task Learning for Interpretation of Brain Decoding Models.- The New Graph Kernels on Connectivity Networks for Identification of MCI.- Mapping Tractography Across Subjects.- Speech.- Automated speech analysis for psychosis evaluation.- Combining different modalities in classifying phonological categories.- Clinics and cognition.- Label-alignment-based Multi-task Feature Selection for Multimodal Classification of Brain Disease.- Leveraging Clinical Data to Enhance Localization of Brain Atrophy.- Estimating Learning Effects: A Short-Time Fourier Transform Regression Model for MEG Source Localization.- Causality and time-series.- Classification-based Causality Detection in Time Series.- Fast and Improved SLEX Analysis of High-dimensional Time Series.- Best paper awards: MLINI 2013.- Predicting Short-Term Cognitive Change from Longitudinal Neuroimaging Analysis.- Hyperalignment of Multi-Subject fMRI Data by Synchronized Projections.- An oblique approach to prediction of conversion to Alzheimer's Disease with multikernel Gaussian Processes.
Автор: Guorong Wu; Paul Laurienti; Leonardo Bonilha; Bren Название: Connectomics in NeuroImaging ISBN: 3319671588 ISBN-13(EAN): 9783319671581 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the refereed proceedings of the First International Workshop on Connectomics in NeuroImaging, CNI 2017, held in conjunction with MICCAI 2017 in Quebec City, Canada, in September 2017. The 19 full papers presented were carefully reviewed and selected from 26 submissions.
Описание: This book constitutes the refereed proceedings of the Second International Workshop on Context-Aware Surgical Theaters, OR 2.0 2019, and the Second International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019.
Автор: Guorong Wu; Islem Rekik; Markus D. Schirmer; Ai We Название: Connectomics in NeuroImaging ISBN: 3030007545 ISBN-13(EAN): 9783030007546 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the refereed proceedings of the Second International Workshop on Connectomics in NeuroImaging, CNI 2018, held in conjunction with MICCAI 2018 in Granada, Spain, in September 2018. The 15 full papers presented were carefully reviewed and selected from 20 submissions. The papers deal with new advancements in network construction, analysis, and visualization techniques in connectomics and their use in clinical diagnosis and group comparison studies as well as in various neuroimaging applications.
Автор: Georg Langs; Irina Rish; Moritz Grosse-Wentrup; Br Название: Machine Learning and Interpretation in Neuroimaging ISBN: 3642347126 ISBN-13(EAN): 9783642347122 Издательство: Springer Рейтинг: Цена: 6429.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This state-of-the-art survey offers a collection of papers from the Workshop on Machine Learning and Interpretation in Neuroimaging, MLINI 2011, held at the 25th Annual Conference on Neural Information Processing, NIPS 2011, in the Sierra Nevada, Spain, in December 2011.
Автор: Wu, Guorong Название: Connectomics ISBN: 0128138386 ISBN-13(EAN): 9780128138380 Издательство: Elsevier Science Рейтинг: Цена: 16505.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Connectomics: Methods, Mathematical Models and Applications is unique in combining a broad introduction to methods in connectomics with neuro- applications. In Part 1 the book explains the importance of connectomics using brain connectivity maps and then outlines the historical advancements in connectivity analysis, showing how these are related to imaging modalities such as diffusion and functional MRI. It then summarizes connectivity analysis approaches that apply different mathematical modeling techniques, such as linear models with regularization, deep-learning models, and graph models.
In part 2 the book describes state-of-the-art research that applies brain connectivity analysis techniques to a broad range of neurological and psychiatric disorders (Alzheimer's, epilepsy, stroke, autism, Parkinson's, traumatic brain injury, drug or alcohol addiction, depression, bipolar, and schizophrenia), brain finger-print applications, speech-language assessments, cognitive assessment, as well as how the connectome can be used to predict demographic information (such as age, gender, or race).
With this book the reader will learn:
The historical development of connectomics together with state-of-the-art methods
How connectomics is applied to a wide range of neuro-applications.
This book is an ideal reference for researchers and graduate students in computer science, data science, computational neuroscience, computational physics or mathematics who need to understand how computational models derived from brain connectivity data are being used in clinical applications, as well as neuroscientists and medical researchers wanting an overview of the technical methods.
Combines connectomics methods with relevant and interesting neuro-applications
Appeals to researchers in a wide range of disciplines: computer science, engineering, data science, mathematics, computational physics, computational neuroscience, as well as neuroscience and medical researchers interested in the technical methods of connectomics
Includes a mathematics primer that formulates connectomics from an applied point-of-view, avoiding a difficult to understand theoretical perspective
An introduction to popular machine learning techniques that enable diagnostic values to connectomics Information on publically available software tools that are used to construct, analyze, and visualize connectome data
Lists of publically available neuro-imaging datasets that can be used to construct structural and functional connectomes
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