Machine Learning in Clinical Neuroimaging
6th International Workshop, MLCN 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings
Published on 8. October 2023
Book
Paperback/Softback
X, 174 pages
978-3-031-44857-7 (ISBN)
Description
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.
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.
More details
Series
Edition
1st ed. 2023
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
47 farbige Abbildungen, 5 s/w Abbildungen
X, 174 p. 52 illus., 47 illus. in color.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 11 mm
Weight
289 gr
ISBN-13
978-3-031-44857-7 (9783031448577)
DOI
10.1007/978-3-031-44858-4
Schweitzer Classification
Other editions
Additional editions
Ahmed Abdulkadir | Deepti R. Bathula | Nicha C. Dvornek
Machine Learning in Clinical Neuroimaging
6th International Workshop, MLCN 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings
E-Book
10/2023
Springer
€58.84
Available for download
Content
Machine Learning.-
Image-to-Image Translation between Tau Pathology and Neuronal Metabolism PET in Alzheimer Disease with Multi-Domain Contrastive Learning.- Multi-Shell dMRI Estimation from Single-Shell Data via Deep Learning.- A Three-Player GAN for Super-Resolution in Magnetic Resonance Imaging.- Cross-Attention for Improved Motion Correction in Brain PET.- VesselShot: Few-shot learning for cerebral blood vessel segmentation.- WaveSep: A Flexible Wavelet-based Approach for Source Separation in Susceptibility Imaging.- Joint Estimation of Neural Events and Hemodynamic Response Functions from Task fMRI via Convolutional Neural Networks.- Learning Sequential Information in Task-based fMRI for Synthetic Data Augmentation.-
Clinical Applications.-
Causal Sensitivity Analysis for Hidden Confounding: Modeling the Sex-Specific Role of Diet on the Aging Brain.- MixUp brain-cortical augmentations in self-supervised learning.- Brain age prediction based on head computed tomography segmentation.- Pretraining is All You Need: A Multi-Atlas Enhanced Transformer Framework for Autism Spectrum Disorder Classification.- Copy Number Variation Informs fMRI-based Prediction of Autism Spectrum Disorder.- Deep attention assisted multi-resolution networks for the segmentation of white matter hyperintensities in postmortem MRI scans .- Stroke outcome and evolution prediction from CT brain using a spatiotemporal diffusion autoencoder.- Morphological versus Functional Network Organization: A Comparison Between Structural Covariance Networks and Probabilistic Functional Modes.