Cantitate/Preț
Produs

Machine Learning in Clinical Neuroimaging: 6th International Workshop, MLCN 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings: Lecture Notes in Computer Science, cartea 14312

Editat de Ahmed Abdulkadir, Deepti R. Bathula, Nicha C. Dvornek, Sindhuja T. Govindarajan, Mohamad Habes, Vinod Kumar, Esten Leonardsen, Thomas Wolfers, Yiming Xiao
en Limba Engleză Paperback – 8 oct 2023
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.
Citește tot Restrânge

Din seria Lecture Notes in Computer Science

Preț: 31766 lei

Preț vechi: 39707 lei
-20% Nou

Puncte Express: 476

Preț estimativ în valută:
6079 6375$ 5069£

Carte tipărită la comandă

Livrare economică 08-22 ianuarie 25

Preluare comenzi: 021 569.72.76

Specificații

ISBN-13: 9783031448577
ISBN-10: 303144857X
Pagini: 174
Ilustrații: X, 174 p. 52 illus., 47 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.27 kg
Ediția:1st ed. 2023
Editura: Springer Nature Switzerland
Colecția Springer
Seria Lecture Notes in Computer Science

Locul publicării:Cham, Switzerland

Cuprins

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.