Graph Learning for Brain Imaging
Author: Feng Liu
Publisher: Frontiers Media SA
Published: 2022-09-30
Total Pages: 141
ISBN-13: 2832501346
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Graph Learning in Medical Imaging
Author: Daoqiang Zhang
Publisher: Springer
Published: 2019-11-14
Total Pages: 182
ISBN-13: 9783030358167
DOWNLOAD EBOOK →This book constitutes the refereed proceedings of the First International Workshop on Graph Learning in Medical Imaging, GLMI 2019, held in conjunction with MICCAI 2019 in Shenzhen, China, in October 2019. The 21 full papers presented were carefully reviewed and selected from 42 submissions. The papers focus on major trends and challenges of graph learning in medical imaging and present original work aimed to identify new cutting-edge techniques and their applications in medical imaging.
Graph Learning in Medical Imaging
Author: Daoqiang Zhang
Publisher: Springer Nature
Published: 2019-11-13
Total Pages: 182
ISBN-13: 3030358178
DOWNLOAD EBOOK →This book constitutes the refereed proceedings of the First International Workshop on Graph Learning in Medical Imaging, GLMI 2019, held in conjunction with MICCAI 2019 in Shenzhen, China, in October 2019. The 21 full papers presented were carefully reviewed and selected from 42 submissions. The papers focus on major trends and challenges of graph learning in medical imaging and present original work aimed to identify new cutting-edge techniques and their applications in medical imaging.
Towards Generalizable Machine Learning in Neuroscience Using Graph Neural Networks
Author: Paul Wang
Publisher:
Published: 2021
Total Pages: 52
ISBN-13:
DOWNLOAD EBOOK →Machine learning and neuroscience have enjoyed a golden era of prosperity over the past decade as the perfect confluence of technological advances have enabled extraordinary experiments and discovery. Though tightly intertwined in the past, advances in both fields have largely diverged such that the application of deep learning techniques to microscopic neural systems remains relatively unexplored. In this thesis, I present work bridging recent advances in machine learning and neuroscience. Specifically, relying on recent advances in whole-brain imaging, we examined the performance of deep learning models on microscopic neural dynamics and resulting emergent behaviors using calcium imaging data from the nematode C. elegans. We show that neural networks perform remarkably well on both neuron-level dynamics prediction and behavioral state classification. In addition, we compared the performance of structure agnostic neural networks and graph neural networks to investigate if graph structure can be exploited as a favourable inductive bias. To perform this experiment, we designed a graph neural network which explicitly infers relations between neurons from neural activity and leverages the inferred graph structure during computations. In our experiments, we found that graph neural networks generally outperformed structure agnostic models and excel in generalization on unseen C. elegans worms. These results imply a potential path to generalizable machine learning in neuroscience where pre-trained models are evaluated on unseen individuals.
Graphs in Biomedical Image Analysis and Integrating Medical Imaging and Non-Imaging Modalities
Author: Danail Stoyanov
Publisher: Springer
Published: 2018-09-15
Total Pages: 101
ISBN-13: 3030006891
DOWNLOAD EBOOK →This book constitutes the refereed joint proceedings of the Second International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2018 and the First International Workshop on Integrating Medical Imaging and Non-Imaging Modalities, Beyond MIC 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 6 full papers presented at GRAIL 2018 and the 5 full papers presented at BeYond MIC 2018 were carefully reviewed and selected. The GRAIL papers cover a wide range of develop graph-based models for the analysis of biomedical images and encourage the exploration of graph-based models for difficult clinical problems within a variety of biomedical imaging contexts. The Beyond MIC papers cover topics of novel methods with significant imaging and non-imaging components, addressing practical applications and new datasets
Fundamentals of Brain Network Analysis
Author: Alex Fornito
Publisher: Academic Press
Published: 2016-03-04
Total Pages: 496
ISBN-13: 0124081185
DOWNLOAD EBOOK →Fundamentals of Brain Network Analysis is a comprehensive and accessible introduction to methods for unraveling the extraordinary complexity of neuronal connectivity. From the perspective of graph theory and network science, this book introduces, motivates and explains techniques for modeling brain networks as graphs of nodes connected by edges, and covers a diverse array of measures for quantifying their topological and spatial organization. It builds intuition for key concepts and methods by illustrating how they can be practically applied in diverse areas of neuroscience, ranging from the analysis of synaptic networks in the nematode worm to the characterization of large-scale human brain networks constructed with magnetic resonance imaging. This text is ideally suited to neuroscientists wanting to develop expertise in the rapidly developing field of neural connectomics, and to physical and computational scientists wanting to understand how these quantitative methods can be used to understand brain organization. Winner of the 2017 PROSE Award in Biomedicine & Neuroscience and the 2017 British Medical Association (BMA) Award in Neurology Extensively illustrated throughout by graphical representations of key mathematical concepts and their practical applications to analyses of nervous systems Comprehensively covers graph theoretical analyses of structural and functional brain networks, from microscopic to macroscopic scales, using examples based on a wide variety of experimental methods in neuroscience Designed to inform and empower scientists at all levels of experience, and from any specialist background, wanting to use modern methods of network science to understand the organization of the brain
Machine Learning in Clinical Neuroimaging
Author: Ahmed Abdulkadir
Publisher: Springer Nature
Published: 2021-09-22
Total Pages: 185
ISBN-13: 3030875865
DOWNLOAD EBOOK →This book constitutes the refereed proceedings of the 4th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2021, held on September 27, 2021, in conjunction with MICCAI 2021. The workshop was held virtually due to the COVID-19 pandemic. The 17 papers presented in this book were carefully reviewed and selected from 27 submissions. They were organized in topical sections named: computational anatomy and brain networks and time series.
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis
Author: Carole H. Sudre
Publisher: Springer Nature
Published: 2020-10-05
Total Pages: 233
ISBN-13: 3030603652
DOWNLOAD EBOOK →This book constitutes the refereed proceedings of the Second International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2020, and the Third International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The workshops were held virtually due to the COVID-19 pandemic. For UNSURE 2020, 10 papers from 18 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world. GRAIL 2020 accepted 10 papers from the 12 submissions received. The workshop aims to bring together scientists that use and develop graph-based models for the analysis of biomedical images and to encourage the exploration of graph-based models for difficult clinical problems within a variety of biomedical imaging contexts.
Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics
Author: M. Jorge Cardoso
Publisher: Springer
Published: 2017-09-06
Total Pages: 262
ISBN-13: 331967675X
DOWNLOAD EBOOK →This book constitutes the refereed joint proceedings of the First International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2017, the 6th International Workshop on Mathematical Foundations of Computational Anatomy, MFCA 2017, and the Third International Workshop on Imaging Genetics, MICGen 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Québec City, QC, Canada, in September 2017. The 7 full papers presented at GRAIL 2017, the 10 full papers presented at MFCA 2017, and the 5 full papers presented at MICGen 2017 were carefully reviewed and selected. The GRAIL papers cover a wide range of graph based medical image analysis methods and applications, including probabilistic graphical models, neuroimaging using graph representations, machine learning for diagnosis prediction, and shape modeling. The MFCA papers deal with theoretical developments in non-linear image and surface registration in the context of computational anatomy. The MICGen papers cover topics in the field of medical genetics, computational biology and medical imaging.
Networks of the Brain
Author: Olaf Sporns
Publisher: MIT Press
Published: 2016-02-12
Total Pages: 433
ISBN-13: 0262528983
DOWNLOAD EBOOK →An integrative overview of network approaches to neuroscience explores the origins of brain complexity and the link between brain structure and function. Over the last decade, the study of complex networks has expanded across diverse scientific fields. Increasingly, science is concerned with the structure, behavior, and evolution of complex systems ranging from cells to ecosystems. In Networks of the Brain, Olaf Sporns describes how the integrative nature of brain function can be illuminated from a complex network perspective. Highlighting the many emerging points of contact between neuroscience and network science, the book serves to introduce network theory to neuroscientists and neuroscience to those working on theoretical network models. Sporns emphasizes how networks connect levels of organization in the brain and how they link structure to function, offering an informal and nonmathematical treatment of the subject. Networks of the Brain provides a synthesis of the sciences of complex networks and the brain that will be an essential foundation for future research.
