Machine Learning in Clinical Neuroimaging (Paperback)

Ahmed Abdulkadir

ISBN 10: 303117898X / ISBN 13: 9783031178986

Published by Springer International Publishing AG, Cham, 2022

About this Item

Paperback. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Seller Inventory # 9783031178986

Bibliographic Details

• Title: Machine Learning in Clinical Neuroimaging (Paperback)
• Publisher: Springer International Publishing AG, Cham
• Publication Date: 2022
• Language: English
• ISBN 10: 303117898X
• ISBN 13: 9783031178986
• Binding: Paperback
• Edition: 1st Edition
• Condition: new

About this title

Synopsis

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.