Functional Brain Network Analysis Based on Unsupervised Deep Learning PDF Download
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Author: Qinglin Dong
Publisher:
ISBN:
Category :
Languages : en
Pages : 198
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Book Description
In the neuroimaging and brain mapping communities, researchers have proposed a variety of computational methods and tools to learn functional brain network (FBN), such as general linear models (GLM), independent component analysis (ICA) and sparse dictionary learning (SDL). Recently, deep learning has attracted much attention in the fields of machine learning and data mining, and it has been proven that deep learning approach has superb representation power over traditional shallow models. In this research, three deep models, which are volumetric sparse deep belief networks (VS-DBN), neural architecture search based DBN (NAS-DBN) and recurrent autoencoder (RAE), were designed to explore representations of fMRI volumes. The quantitative analysis showed that these deep models have promising capability in learning meaningful FBNs and revealed novel insights into the organizational architecture of human brain.
Author: Qinglin Dong
Publisher:
ISBN:
Category :
Languages : en
Pages : 198
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Book Description
In the neuroimaging and brain mapping communities, researchers have proposed a variety of computational methods and tools to learn functional brain network (FBN), such as general linear models (GLM), independent component analysis (ICA) and sparse dictionary learning (SDL). Recently, deep learning has attracted much attention in the fields of machine learning and data mining, and it has been proven that deep learning approach has superb representation power over traditional shallow models. In this research, three deep models, which are volumetric sparse deep belief networks (VS-DBN), neural architecture search based DBN (NAS-DBN) and recurrent autoencoder (RAE), were designed to explore representations of fMRI volumes. The quantitative analysis showed that these deep models have promising capability in learning meaningful FBNs and revealed novel insights into the organizational architecture of human brain.
Author: James C. Gee
Publisher: Springer
ISBN: 364238868X
Category : Computers
Languages : en
Pages : 802
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Book Description
This book constitutes the proceedings of the 23rd International Conference on Information Processing in Medical Imaging, IPMI 2013, held in Asilomar in June/July 2013. The 26 full papers and 38 poster papers presented in this volume were carefully reviewed and selected from 199 submissions. The papers are organized in topical sections on connectivity, groupwise registration, neuro segmentation, statistical analysis, dynamic imaging, cortical surface registration, diffusion MRI, functional imaging, torso image analysis, and tract analysis.
Author: Rong Chen
Publisher: Frontiers Media SA
ISBN: 2889666832
Category : Science
Languages : en
Pages : 290
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Book Description
Author: Shihui Ying
Publisher: Frontiers Media SA
ISBN: 2832546153
Category : Science
Languages : en
Pages : 147
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Book Description
Author: Robert Kozma
Publisher: Academic Press
ISBN: 0128162503
Category : Science
Languages : en
Pages : 352
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Book Description
Artificial Intelligence in the Age of Neural Networks and Brain Computing demonstrates that existing disruptive implications and applications of AI is a development of the unique attributes of neural networks, mainly machine learning, distributed architectures, massive parallel processing, black-box inference, intrinsic nonlinearity and smart autonomous search engines. The book covers the major basic ideas of brain-like computing behind AI, provides a framework to deep learning, and launches novel and intriguing paradigms as future alternatives. The success of AI-based commercial products proposed by top industry leaders, such as Google, IBM, Microsoft, Intel and Amazon can be interpreted using this book. Developed from the 30th anniversary of the International Neural Network Society (INNS) and the 2017 International Joint Conference on Neural Networks (IJCNN) Authored by top experts, global field pioneers and researchers working on cutting-edge applications in signal processing, speech recognition, games, adaptive control and decision-making Edited by high-level academics and researchers in intelligent systems and neural networks
Author: Geoffrey Hinton
Publisher: MIT Press
ISBN: 9780262581684
Category : Medical
Languages : en
Pages : 420
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Book Description
Since its founding in 1989 by Terrence Sejnowski, Neural Computation has become the leading journal in the field. Foundations of Neural Computation collects, by topic, the most significant papers that have appeared in the journal over the past nine years. This volume of Foundations of Neural Computation, on unsupervised learning algorithms, focuses on neural network learning algorithms that do not require an explicit teacher. The goal of unsupervised learning is to extract an efficient internal representation of the statistical structure implicit in the inputs. These algorithms provide insights into the development of the cerebral cortex and implicit learning in humans. They are also of interest to engineers working in areas such as computer vision and speech recognition who seek efficient representations of raw input data.
Author: Céline Gravelines
Publisher:
ISBN:
Category :
Languages : en
Pages : 152
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Book Description
Functional brain parcellation - the delineation of brain regions based on functional connectivity - is an active research area lacking an ideal subject-specific solution independent of anatomical composition, manual feature engineering, or heavily labelled examples. Deep learning is a cutting-edge area of machine learning on the forefront of current artificial intelligence developments. Specifically, autoencoders are artificial neural networks which can be stacked to form hierarchical sparse deep models from which high-level features are compressed, organized, and extracted, without labelled training data, allowing for unsupervised learning. This thesis presents a novel application of stacked sparse autoencoders to the problem of parcellating the brain based on its components' (voxels') functional connectivity, focusing on the medial parietal cortex. Various depths of autoencoders are investigated, yielding results of up to (68 ± 3)% accuracy compared with ground truth parcellations using Dice's coefficient. This data-driven functional parcellation technique offers promising growth to both the neuroimaging and machine learning communities.
Author: S. Kevin Zhou
Publisher: Academic Press
ISBN: 0323858880
Category : Computers
Languages : en
Pages : 544
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Book Description
Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for academic and industry researchers and graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Deep learning provides exciting solutions for medical image analysis problems and is a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component are applied to medical image detection, segmentation, registration, and computer-aided analysis. · Covers common research problems in medical image analysis and their challenges · Describes the latest deep learning methods and the theories behind approaches for medical image analysis · Teaches how algorithms are applied to a broad range of application areas including cardiac, neural and functional, colonoscopy, OCTA applications and model assessment · Includes a Foreword written by Nicholas Ayache
Author: Dajiang Zhu
Publisher: Frontiers Media SA
ISBN: 2832547575
Category : Science
Languages : en
Pages : 230
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Book Description
Brain mapping is dedicated to using brain imaging techniques such as MRI, CT, PET, EEG, and fNIRS to understand the brain anatomy, structure, and function, and how it contributes to cognition, behavior, and deficits of brain diseases. Recently, machine learning is in a stage of rapid development, and various new technologies are continuously introduced into the field, from traditional approaches
Author: Fouad Sabry
Publisher: One Billion Knowledgeable
ISBN:
Category : Computers
Languages : en
Pages : 186
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Book Description
What Is Artificial Neural Networks Computing systems that are inspired by the biological neural networks that make up animal brains are called artificial neural networks (ANNs). These systems are more commonly referred to as neural networks (NNs) or neural nets. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Artificial neural network Chapter 2: Artificial neuron Chapter 3: Unsupervised learning Chapter 4: Backpropagation Chapter 5: Types of artificial neural networks Chapter 6: Deep learning Chapter 7: Convolutional neural network Chapter 8: Long short-term memory Chapter 9: Recurrent neural network Chapter 10: History of artificial neural networks (II) Answering the public top questions about artificial neural networks. (III) Real world examples for the usage of artificial neural networks in many fields. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of artificial neural networks. What Is Artificial Intelligence Series The Artificial Intelligence eBook series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field. The Artificial Intelligence eBook series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.
