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E-grāmata: Machine Learning in Clinical Neuroimaging: 6th International Workshop, MLCN 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings

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  • Formāts: PDF+DRM
  • Sērija : Lecture Notes in Computer Science 14312
  • Izdošanas datums: 07-Oct-2023
  • Izdevniecība: Springer International Publishing AG
  • Valoda: eng
  • ISBN-13: 9783031448584
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Machine Learning in Clinical Neuroimaging: 6th International Workshop, MLCN 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, ProceedingsPalielināt
  • Formāts: PDF+DRM
  • Sērija : Lecture Notes in Computer Science 14312
  • Izdošanas datums: 07-Oct-2023
  • Izdevniecība: Springer International Publishing AG
  • Valoda: eng
  • ISBN-13: 9783031448584
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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.- 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.
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