Machine Learning in Clinical Neuroimaging
portes grátis
Machine Learning in Clinical Neuroimaging
6th International Workshop, MLCN 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings
Abdulkadir, Ahmed; Wolfers, Thomas; Dvornek, Nicha C.; Bathula, Deepti R.; Govindarajan, Sindhuja T.; Xiao, Yiming; Leonardsen, Esten; Habes, Mohamad; Kumar, Vinod
Springer International Publishing AG
10/2023
174
Mole
Inglês
9783031448577
15 a 20 dias
Descrição não disponível.
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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artificial intelligence;bioinformatics;computer networks;computer science;computer systems;computer vision;deep learning;image analysis;image processing;image reconstruction;image segmentation;machine learning;neural networks;object recognition;object segmentation;pattern recognition;segmentation methods
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.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
artificial intelligence;bioinformatics;computer networks;computer science;computer systems;computer vision;deep learning;image analysis;image processing;image reconstruction;image segmentation;machine learning;neural networks;object recognition;object segmentation;pattern recognition;segmentation methods
