Machine Learning In Clinical Neuroscience

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Machine Learning in Clinical Neuroscience

Victor E. Staartjes,Luca Regli,Carlo Serra
Machine Learning in Clinical Neuroscience Book PDF

Author : Victor E. Staartjes,Luca Regli,Carlo Serra
Publisher : Springer Nature
Page : 343 pages
File Size : 54,8 Mb
Release : 2021-12-03
Category : Medical
ISBN : 9783030852924

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Machine Learning in Clinical Neuroscience by Victor E. Staartjes,Luca Regli,Carlo Serra Pdf

This book bridges the gap between data scientists and clinicians by introducing all relevant aspects of machine learning in an accessible way, and will certainly foster new and serendipitous applications of machine learning in the clinical neurosciences. Building from the ground up by communicating the foundational knowledge and intuitions first before progressing to more advanced and specific topics, the book is well-suited even for clinicians without prior machine learning experience. Authored by a wide array of experienced global machine learning groups, the book is aimed at clinicians who are interested in mastering the basics of machine learning and who wish to get started with their own machine learning research. The volume is structured in two major parts: The first uniquely introduces all major concepts in clinical machine learning from the ground up, and includes step-by-step instructions on how to correctly develop and validate clinical prediction models. It also includes methodological and conceptual foundations of other applications of machine learning in clinical neuroscience, such as applications of machine learning to neuroimaging, natural language processing, and time series analysis. The second part provides an overview of some state-of-the-art applications of these methodologies. The Machine Intelligence in Clinical Neuroscience (MICN) Laboratory at the Department of Neurosurgery of the University Hospital Zurich studies clinical applications of machine intelligence to improve patient care in clinical neuroscience. The group focuses on diagnostic, prognostic and predictive analytics that aid in decision-making by increasing objectivity and transparency to patients. Other major interests of our group members are in medical imaging, and intraoperative applications of machine vision.

Machine Learning in Clinical Neuroimaging

Ahmed Abdulkadir,Deepti R. Bathula,Nicha C. Dvornek,Sindhuja T. Govindarajan,Mohamad Habes,Vinod Kumar,Esten Leonardsen,Thomas Wolfers,Yiming Xiao
Machine Learning in Clinical Neuroimaging Book PDF

Author : Ahmed Abdulkadir,Deepti R. Bathula,Nicha C. Dvornek,Sindhuja T. Govindarajan,Mohamad Habes,Vinod Kumar,Esten Leonardsen,Thomas Wolfers,Yiming Xiao
Publisher : Springer Nature
Page : 183 pages
File Size : 52,5 Mb
Release : 2023-10-07
Category : Computers
ISBN : 9783031448584

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Machine Learning in Clinical Neuroimaging by Ahmed Abdulkadir,Deepti R. Bathula,Nicha C. Dvornek,Sindhuja T. Govindarajan,Mohamad Habes,Vinod Kumar,Esten Leonardsen,Thomas Wolfers,Yiming Xiao Pdf

This book constitutes the refereed proceedings of the 6th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2023, held in Conjunction with MICCAI 2023 in Vancouver, Canada, in October 2023. The book includes 16 papers which were carefully reviewed and selected from 28 full-length submissions. The 6th International Workshop on Machine Learning in Clinical Neuroimaging (MLCN 2023) aims to bring together the top researchers in both machine learning and clinical neuroscience as well as tech-savvy clinicians to address two main challenges: 1) development of methodological approaches for analyzing complex and heterogeneous neuroimaging data (machine learning track); and 2) filling the translational gap in applying existing machine learning methods in clinical practices (clinical neuroimaging track). The papers are categorzied into topical sub-headings on Machine Learning and Clinical Applications.

Machine Learning in Clinical Neuroimaging

Ahmed Abdulkadir,Deepti R. Bathula,Nicha C. Dvornek,Mohamad Habes,Seyed Mostafa Kia,Vinod Kumar,Thomas Wolfers
Machine Learning in Clinical Neuroimaging Book PDF

Author : Ahmed Abdulkadir,Deepti R. Bathula,Nicha C. Dvornek,Mohamad Habes,Seyed Mostafa Kia,Vinod Kumar,Thomas Wolfers
Publisher : Springer Nature
Page : 190 pages
File Size : 48,5 Mb
Release : 2022-10-07
Category : Computers
ISBN : 9783031178993

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Machine Learning in Clinical Neuroimaging by Ahmed Abdulkadir,Deepti R. Bathula,Nicha C. Dvornek,Mohamad Habes,Seyed Mostafa Kia,Vinod Kumar,Thomas Wolfers Pdf

This book constitutes the refereed proceedings of the 5th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2022, held in Conjunction with MICCAI 2022, Singapore in September 2022. The book includes 17 papers which were carefully reviewed and selected from 23 full-length submissions. The 5th international workshop on Machine Learning in Clinical Neuroimaging (MLCN2022) aims to bring together the top researchers in both machine learning and clinical neuroscience as well as tech-savvy clinicians to address two main challenges: 1) development of methodological approaches for analyzing complex and heterogeneous neuroimaging data (machine learning track); and 2) filling the translational gap in applying existing machine learning methods in clinical practices (clinical neuroimaging track). The papers are categorzied into topical sub-headings: Morphometry; Diagnostics, and Aging, and Neurodegeneration.

Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology

Seyed Mostafa Kia,Hassan Mohy-ud-Din,Ahmed Abdulkadir,Cher Bass,Mohamad Habes,Jane Maryam Rondina,Chantal Tax,Hongzhi Wang,Thomas Wolfers,Saima Rathore,Madhura Ingalhalikar
Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro oncology Book PDF

Author : Seyed Mostafa Kia,Hassan Mohy-ud-Din,Ahmed Abdulkadir,Cher Bass,Mohamad Habes,Jane Maryam Rondina,Chantal Tax,Hongzhi Wang,Thomas Wolfers,Saima Rathore,Madhura Ingalhalikar
Publisher : Springer Nature
Page : 319 pages
File Size : 42,5 Mb
Release : 2020-12-30
Category : Computers
ISBN : 9783030668433

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Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology by Seyed Mostafa Kia,Hassan Mohy-ud-Din,Ahmed Abdulkadir,Cher Bass,Mohamad Habes,Jane Maryam Rondina,Chantal Tax,Hongzhi Wang,Thomas Wolfers,Saima Rathore,Madhura Ingalhalikar Pdf

This book constitutes the refereed proceedings of the Third International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2020, and the Second International Workshop on Radiogenomics in Neuro-oncology, RNO-AI 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020.* For MLCN 2020, 18 papers out of 28 submissions were accepted for publication. The accepted papers present novel contributions in both developing new machine learning methods and applications of existing methods to solve challenging problems in clinical neuroimaging. For RNO-AI 2020, all 8 submissions were accepted for publication. They focus on addressing the problems of applying machine learning to large and multi-site clinical neuroimaging datasets. The workshop aimed to bring together experts in both machine learning and clinical neuroimaging to discuss and hopefully bridge the existing challenges of applied machine learning in clinical neuroscience. *The workshops were held virtually due to the COVID-19 pandemic.

Big Data in Psychiatry and Neurology

Ahmed Moustafa
Big Data in Psychiatry and Neurology Book PDF

Author : Ahmed Moustafa
Publisher : Academic Press
Page : 386 pages
File Size : 52,6 Mb
Release : 2021-06-11
Category : Medical
ISBN : 9780128230022

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Big Data in Psychiatry and Neurology by Ahmed Moustafa Pdf

Big Data in Psychiatry and Neurology provides an up-to-date overview of achievements in the field of big data in Psychiatry and Medicine, including applications of big data methods to aging disorders (e.g., Alzheimer’s disease and Parkinson’s disease), mood disorders (e.g., major depressive disorder), and drug addiction. This book will help researchers, students and clinicians implement new methods for collecting big datasets from various patient populations. Further, it will demonstrate how to use several algorithms and machine learning methods to analyze big datasets, thus providing individualized treatment for psychiatric and neurological patients. As big data analytics is gaining traction in psychiatric research, it is an essential component in providing predictive models for both clinical practice and public health systems. As compared with traditional statistical methods that provide primarily average group-level results, big data analytics allows predictions and stratification of clinical outcomes at an individual subject level. Discusses longitudinal big data and risk factors surrounding the development of psychiatric disorders Analyzes methods in using big data to treat psychiatric and neurological disorders Describes the role machine learning can play in the analysis of big data Demonstrates the various methods of gathering big data in medicine Reviews how to apply big data to genetics

Machine Learning for Brain Disorders

Olivier Colliot
Machine Learning for Brain Disorders Book PDF

Author : Olivier Colliot
Publisher : Springer Nature
Page : 1058 pages
File Size : 53,5 Mb
Release : 2023-07-24
Category : Medical
ISBN : 9781071631959

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Machine Learning for Brain Disorders by Olivier Colliot Pdf

This Open Access volume provides readers with an up-to-date and comprehensive guide to both methodological and applicative aspects of machine learning (ML) for brain disorders. The chapters in this book are 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. In the Neuromethods series style, chapters include the kind of detail and key advice from the specialists needed to get successful results in your laboratory. Comprehensive and cutting, Machine Learning for Brain Disorders is a valuable resource for researchers and graduate students who are new to this field, as well as experienced researchers who would like to further expand their knowledge in this area. This book will be useful to students and researchers from various backgrounds such as engineers, computer scientists, neurologists, psychiatrists, radiologists, and neuroscientists.

Machine Learning and Medical Imaging

Guorong Wu,Dinggang Shen,Mert Sabuncu
Machine Learning and Medical Imaging Book PDF

Author : Guorong Wu,Dinggang Shen,Mert Sabuncu
Publisher : Academic Press
Page : 512 pages
File Size : 49,6 Mb
Release : 2016-08-11
Category : Technology & Engineering
ISBN : 9780128041147

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Machine Learning and Medical Imaging by Guorong Wu,Dinggang Shen,Mert Sabuncu Pdf

Machine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical image analysis. It first summarizes cutting-edge machine learning algorithms in medical imaging, including not only classical probabilistic modeling and learning methods, but also recent breakthroughs in deep learning, sparse representation/coding, and big data hashing. In the second part leading research groups around the world present a wide spectrum of machine learning methods with application to different medical imaging modalities, clinical domains, and organs. The biomedical imaging modalities include ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), histology, and microscopy images. The targeted organs span the lung, liver, brain, and prostate, while there is also a treatment of examining genetic associations. Machine Learning and Medical Imaging is an ideal reference for medical imaging researchers, industry scientists and engineers, advanced undergraduate and graduate students, and clinicians. Demonstrates the application of cutting-edge machine learning techniques to medical imaging problems Covers an array of medical imaging applications including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics Features self-contained chapters with a thorough literature review Assesses the development of future machine learning techniques and the further application of existing techniques

The Cambridge Handbook of Research Methods in Clinical Psychology

Aidan G. C. Wright,Michael N. Hallquist
The Cambridge Handbook of Research Methods in Clinical Psychology Book PDF

Author : Aidan G. C. Wright,Michael N. Hallquist
Publisher : Cambridge University Press
Page : 600 pages
File Size : 41,5 Mb
Release : 2020-03-31
Category : Psychology
ISBN : 1316639525

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The Cambridge Handbook of Research Methods in Clinical Psychology by Aidan G. C. Wright,Michael N. Hallquist Pdf

This book integrates philosophy of science, data acquisition methods, and statistical modeling techniques to present readers with a forward-thinking perspective on clinical science. It reviews modern research practices in clinical psychology that support the goals of psychological science, study designs that promote good research, and quantitative methods that can test specific scientific questions. It covers new themes in research including intensive longitudinal designs, neurobiology, developmental psychopathology, and advanced computational methods such as machine learning. Core chapters examine significant statistical topics, for example missing data, causality, meta-analysis, latent variable analysis, and dyadic data analysis. A balanced overview of observational and experimental designs is also supplied, including preclinical research and intervention science. This is a foundational resource that supports the methodological training of the current and future generations of clinical psychological scientists.

Machine Learning in Clinical Neuroimaging

Ahmed Abdulkadir,Deepti R. Bathula,Nicha C. Dvornek,Mohamad Habes,Seyed Mostafa Kia,Vinod Kumar,Thomas Wolfers
Machine Learning in Clinical Neuroimaging Book PDF

Author : Ahmed Abdulkadir,Deepti R. Bathula,Nicha C. Dvornek,Mohamad Habes,Seyed Mostafa Kia,Vinod Kumar,Thomas Wolfers
Publisher : Unknown
Page : 0 pages
File Size : 45,6 Mb
Release : 2022
Category : Diagnostic imaging
ISBN : 8303117890

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Machine Learning in Clinical Neuroimaging by Ahmed Abdulkadir,Deepti R. Bathula,Nicha C. Dvornek,Mohamad Habes,Seyed Mostafa Kia,Vinod Kumar,Thomas Wolfers Pdf

This book constitutes the refereed proceedings of the 5th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2022, held in Conjunction with MICCAI 2022, Singapore in September 2022. The book includes 17 papers which were carefully reviewed and selected from 23 full-length submissions. The 5th international workshop on Machine Learning in Clinical Neuroimaging (MLCN2022) aims to bring together the top researchers in both machine learning and clinical neuroscience as well as tech-savvy clinicians to address two main challenges: 1) development of methodological approaches for analyzing complex and heterogeneous neuroimaging data (machine learning track); and 2) filling the translational gap in applying existing machine learning methods in clinical practices (clinical neuroimaging track). The papers are categorzied into topical sub-headings: Morphometry; Diagnostics, and Aging, and Neurodegeneration. .

Machine Learning and Deep Learning Techniques for Medical Science

K. Gayathri Devi,Kishore Balasubramanian,Le Anh Ngoc
Machine Learning and Deep Learning Techniques for Medical Science Book PDF

Author : K. Gayathri Devi,Kishore Balasubramanian,Le Anh Ngoc
Publisher : CRC Press
Page : 351 pages
File Size : 41,7 Mb
Release : 2022-05-11
Category : Technology & Engineering
ISBN : 9781000583366

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Machine Learning and Deep Learning Techniques for Medical Science by K. Gayathri Devi,Kishore Balasubramanian,Le Anh Ngoc Pdf

The application of machine learning is growing exponentially into every branch of business and science, including medical science. This book presents the integration of machine learning (ML) and deep learning (DL) algorithms that can be applied in the healthcare sector to reduce the time required by doctors, radiologists, and other medical professionals for analyzing, predicting, and diagnosing the conditions with accurate results. The book offers important key aspects in the development and implementation of ML and DL approaches toward developing prediction tools and models and improving medical diagnosis. The contributors explore the recent trends, innovations, challenges, and solutions, as well as case studies of the applications of ML and DL in intelligent system-based disease diagnosis. The chapters also highlight the basics and the need for applying mathematical aspects with reference to the development of new medical models. Authors also explore ML and DL in relation to artificial intelligence (AI) prediction tools, the discovery of drugs, neuroscience, diagnosis in multiple imaging modalities, and pattern recognition approaches to functional magnetic resonance imaging images. This book is for students and researchers of computer science and engineering, electronics and communication engineering, and information technology; for biomedical engineering researchers, academicians, and educators; and for students and professionals in other areas of the healthcare sector. Presents key aspects in the development and the implementation of ML and DL approaches toward developing prediction tools, models, and improving medical diagnosis Discusses the recent trends, innovations, challenges, solutions, and applications of intelligent system-based disease diagnosis Examines DL theories, models, and tools to enhance health information systems Explores ML and DL in relation to AI prediction tools, discovery of drugs, neuroscience, and diagnosis in multiple imaging modalities Dr. K. Gayathri Devi is a Professor at the Department of Electronics and Communication Engineering, Dr. N.G.P Institute of Technology, Tamil Nadu, India. Dr. Kishore Balasubramanian is an Assistant Professor (Senior Scale) at the Department of EEE at Dr. Mahalingam College of Engineering & Technology, Tamil Nadu, India. Dr. Le Anh Ngoc is a Director of Swinburne Innovation Space and Professor in Swinburne University of Technology (Vietnam).

Machine Learning

Andrea Mechelli,Sandra Vieira
Machine Learning Book PDF

Author : Andrea Mechelli,Sandra Vieira
Publisher : Academic Press
Page : 412 pages
File Size : 44,9 Mb
Release : 2019-11-14
Category : Medical
ISBN : 9780128157404

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Machine Learning by Andrea Mechelli,Sandra Vieira Pdf

Machine Learning is an area of artificial intelligence involving the development of algorithms to discover trends and patterns in existing data; this information can then be used to make predictions on new data. A growing number of researchers and clinicians are using machine learning methods to develop and validate tools for assisting the diagnosis and treatment of patients with brain disorders. Machine Learning: Methods and Applications to Brain Disorders provides an up-to-date overview of how these methods can be applied to brain disorders, including both psychiatric and neurological disease. This book is written for a non-technical audience, such as neuroscientists, psychologists, psychiatrists, neurologists and health care practitioners. Provides a non-technical introduction to machine learning and applications to brain disorders Includes a detailed description of the most commonly used machine learning algorithms as well as some novel and promising approaches Covers the main methodological challenges in the application of machine learning to brain disorders Provides a step-by-step tutorial for implementing a machine learning pipeline to neuroimaging data in Python

OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging

Luping Zhou,Duygu Sarikaya,Seyed Mostafa Kia,Stefanie Speidel,Anand Malpani,Daniel Hashimoto,Mohamad Habes,Tommy Löfstedt,Kerstin Ritter,Hongzhi Wang
OR 2 0 Context Aware Operating Theaters and Machine Learning in Clinical Neuroimaging Book PDF

Author : Luping Zhou,Duygu Sarikaya,Seyed Mostafa Kia,Stefanie Speidel,Anand Malpani,Daniel Hashimoto,Mohamad Habes,Tommy Löfstedt,Kerstin Ritter,Hongzhi Wang
Publisher : Springer Nature
Page : 114 pages
File Size : 40,8 Mb
Release : 2019-10-10
Category : Computers
ISBN : 9783030326951

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OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging by Luping Zhou,Duygu Sarikaya,Seyed Mostafa Kia,Stefanie Speidel,Anand Malpani,Daniel Hashimoto,Mohamad Habes,Tommy Löfstedt,Kerstin Ritter,Hongzhi Wang Pdf

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. For OR 2.0 all 6 submissions were accepted for publication. They aim to highlight the potential use of machine vision and perception, robotics, surgical simulation and modeling, multi-modal data fusion and visualization, image analysis, advanced imaging, advanced display technologies, human-computer interfaces, sensors, wearable and implantable electronics and robots, visual attention models, cognitive models, decision support networks to enhance surgical procedural assistance, context-awareness and team communication in the operating theater, human-robot collaborative systems, and surgical training and assessment. MLCN 2019 accepted 6 papers out of 7 submissions for publication. They focus on addressing the problems of applying machine learning to large and multi-site clinical neuroimaging datasets. The workshop aimed to bring together experts in both machine learning and clinical neuroimaging to discuss and hopefully bridge the existing challenges of applied machine learning in clinical neuroscience.

Understanding and Interpreting Machine Learning in Medical Image Computing Applications

Danail Stoyanov,Zeike Taylor,Seyed Mostafa Kia,Ipek Oguz,Mauricio Reyes,Anne Martel,Lena Maier-Hein,Andre F. Marquand,Edouard Duchesnay,Tommy Löfstedt,Bennett Landman,M. Jorge Cardoso,Carlos A. Silva,Sergio Pereira,Raphael Meier
Understanding and Interpreting Machine Learning in Medical Image Computing Applications Book PDF

Author : Danail Stoyanov,Zeike Taylor,Seyed Mostafa Kia,Ipek Oguz,Mauricio Reyes,Anne Martel,Lena Maier-Hein,Andre F. Marquand,Edouard Duchesnay,Tommy Löfstedt,Bennett Landman,M. Jorge Cardoso,Carlos A. Silva,Sergio Pereira,Raphael Meier
Publisher : Springer
Page : 149 pages
File Size : 53,8 Mb
Release : 2018-10-23
Category : Computers
ISBN : 9783030026288

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Understanding and Interpreting Machine Learning in Medical Image Computing Applications by Danail Stoyanov,Zeike Taylor,Seyed Mostafa Kia,Ipek Oguz,Mauricio Reyes,Anne Martel,Lena Maier-Hein,Andre F. Marquand,Edouard Duchesnay,Tommy Löfstedt,Bennett Landman,M. Jorge Cardoso,Carlos A. Silva,Sergio Pereira,Raphael Meier Pdf

This book constitutes the refereed joint proceedings of the First International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2018, the First International Workshop on Deep Learning Fails, DLF 2018, and the First International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 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 4 full MLCN papers, the 6 full DLF papers, and the 6 full iMIMIC papers included in this volume were carefully reviewed and selected. The MLCN contributions develop state-of-the-art machine learning methods such as spatio-temporal Gaussian process analysis, stochastic variational inference, and deep learning for applications in Alzheimer's disease diagnosis and multi-site neuroimaging data analysis; the DLF papers evaluate the strengths and weaknesses of DL and identify the main challenges in the current state of the art and future directions; the iMIMIC papers cover a large range of topics in the field of interpretability of machine learning in the context of medical image analysis.

Machine Learning in Clinical Neuroimaging

Ahmed Abdulkadir,Seyed Mostafa Kia,Mohamad Habes,Vinod Kumar,Jane Maryam Rondina,Chantal Tax,Thomas Wolfers
Machine Learning in Clinical Neuroimaging Book PDF

Author : Ahmed Abdulkadir,Seyed Mostafa Kia,Mohamad Habes,Vinod Kumar,Jane Maryam Rondina,Chantal Tax,Thomas Wolfers
Publisher : Springer Nature
Page : 185 pages
File Size : 41,7 Mb
Release : 2021-09-22
Category : Computers
ISBN : 9783030875862

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Machine Learning in Clinical Neuroimaging by Ahmed Abdulkadir,Seyed Mostafa Kia,Mohamad Habes,Vinod Kumar,Jane Maryam Rondina,Chantal Tax,Thomas Wolfers Pdf

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.

Machine Learning and Interpretation in Neuroimaging

Georg Langs,Irina Rish,Moritz Grosse-Wentrup,Brian Murphy
Machine Learning and Interpretation in Neuroimaging Book PDF

Author : Georg Langs,Irina Rish,Moritz Grosse-Wentrup,Brian Murphy
Publisher : Springer
Page : 266 pages
File Size : 42,6 Mb
Release : 2012-11-11
Category : Computers
ISBN : 9783642347139

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Machine Learning and Interpretation in Neuroimaging by Georg Langs,Irina Rish,Moritz Grosse-Wentrup,Brian Murphy Pdf

Brain imaging brings together the technology, methodology, research questions and approaches of a wide range of scientific fields including physics, statistics, computer science, neuroscience, biology, and engineering. Thus, methodological and technological advances that enable us to obtain measurements, examine relationships across observations, and link these data to neuroscientific hypotheses happen in a highly interdisciplinary environment. The dynamic field of machine learning with its modern approach to data mining provides many relevant approaches for neuroscience and enables the exploration of open questions. 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. Additionally, invited speakers agreed to contribute reviews on various aspects of the field, adding breadth and perspective to the volume. The 32 revised papers were carefully selected from 48 submissions. At the interface between machine learning and neuroimaging the papers aim at shedding some light on the state of the art in this interdisciplinary field. They are organized in topical sections on coding and decoding, neuroscience, dynamcis, connectivity, and probabilistic models and machine learning.