Machine Learning for Brain Disorders (Record no. 80346)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 04914nam a22004815i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-1-0716-3195-9 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | DE-He213 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240911143916.0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
| fixed length control field | cr nn 008mamaa |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 230725s2023 xxu| s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781071631959 |
| -- | 978-1-0716-3195-9 |
| 024 7# - OTHER STANDARD IDENTIFIER | |
| Standard number or code | 10.1007/978-1-0716-3195-9 |
| Source of number or code | doi |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | RC321-580 |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | PSAN |
| Source | bicssc |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | SCI089000 |
| Source | bisacsh |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | PSAN |
| Source | thema |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 612.8 |
| Edition number | 23 |
| 245 10 - TITLE STATEMENT | |
| Title | Machine Learning for Brain Disorders |
| Medium | [electronic resource] / |
| Statement of responsibility, etc. | edited by Olivier Colliot. |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2023. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | New York, NY : |
| Name of producer, publisher, distributor, manufacturer | Springer US : |
| -- | Imprint: Humana, |
| Date of production, publication, distribution, manufacture, or copyright notice | 2023. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | XXXI, 1047 p. 265 illus., 232 illus. in color. |
| Other physical details | online resource. |
| 336 ## - CONTENT TYPE | |
| Content type term | text |
| Content type code | txt |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type term | computer |
| Media type code | c |
| Source | rdamedia |
| 338 ## - CARRIER TYPE | |
| Carrier type term | online resource |
| Carrier type code | cr |
| Source | rdacarrier |
| 347 ## - DIGITAL FILE CHARACTERISTICS | |
| File type | text file |
| Encoding format | |
| Source | rda |
| 490 1# - SERIES STATEMENT | |
| Series statement | Neuromethods, |
| International Standard Serial Number | 1940-6045 ; |
| Volume/sequential designation | 197 |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | A Non-Technical Introduction to Machine Learning -- Classic Machine Learning Methods -- Deep Learning: Basics and Convolutional Neural Networks (CNN) -- Recurrent Neural Networks (RNN) - Architectures, Training Tricks, and Introduction to Influential Research -- Generative Adversarial Networks and Other Generative Models -- Transformers and Visual Transformers -- Clinical Assessment of Brain Disorders -- Neuroimaging in Machine Learning for Brain Disorders -- Electroencephalography and Magnetoencephalography -- Working with Omics Data, An Interdisciplinary Challenge at the Crossroads of Biology and Computer Science -- Electronic Health Records as Source of Research Data -- Mobile Devices, Connected Objects, and Sensors -- Medical Image Segmentation using Deep Learning -- Image Registration: Fundamentals and Recent Advances Based on Deep Learning -- Computer-Aided Diagnosis and Prediction in Brain Disorders -- Subtyping Brain Diseases from Imaging Data -- Data-Driven Disease Progression Modelling -- Computational Pathology for Brain Disorders -- Integration of Multimodal Data -- Evaluating Machine Learning Models and their Diagnostic Value -- Reproducibility in Machine Learning for Medical Imaging -- Interpretability of Machine Learning Methods Applied to Neuroimaging -- A Regulatory Science Perspective on Performance Assessment of Machine Learning Algorithms in Imaging -- Main Existing Datasets for Open Brain Research on Humans -- Machine Learning for Alzheimer’s Disease and Related Dementias -- Machine Learning for Parkinson’s Disease and Related Disorders -- Machine Learning in Neuroimaging of Epilepsy -- Machine Learning in Multiple Sclerosis -- Machine Learning for Cerebrovascular Disorders -- The Role of Artificial Intelligence in Neuro-Oncology Imaging -- Machine Learning for Neurodevelopmental Disorders -- Machine Learning and BrainImaging for Psychiatric Disorders: New Perspectives. |
| 506 0# - RESTRICTIONS ON ACCESS NOTE | |
| Terms governing access | Open Access |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | 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. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Neurosciences. |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Neuroscience. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Colliot, Olivier. |
| Relator term | editor. |
| Relationship | edt |
| -- | http://id.loc.gov/vocabulary/relators/edt |
| 710 2# - ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | SpringerLink (Online service) |
| 773 0# - HOST ITEM ENTRY | |
| Title | Springer Nature eBook |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Relationship information | Printed edition: |
| International Standard Book Number | 9781071631942 |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Relationship information | Printed edition: |
| International Standard Book Number | 9781071631966 |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Relationship information | Printed edition: |
| International Standard Book Number | 9781071631973 |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| Uniform title | Neuromethods, |
| International Standard Serial Number | 1940-6045 ; |
| Volume/sequential designation | 197 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://doi.org/10.1007/978-1-0716-3195-9">https://doi.org/10.1007/978-1-0716-3195-9</a> |
| 912 ## - | |
| -- | ZDB-2-PRO |
| 912 ## - | |
| -- | ZDB-2-SOB |
| 945 ## - LOCAL PROCESSING INFORMATION (OCLC) | |
| a | 1 |
| b | Madushi Gamage |
| c | 1 |
| d | Madushi Gamage |
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