Learning patterns of the ageing brain in MRI using deep convolutional networks

Learning patterns of the ageing brain in MRI using deep convolutional networks

Both normal ageing and neurodegenerative diseases cause morphological changes to the brain. Age-related brain changes are subtle, nonlinear, and spatially and temporally heterogenous, both within a subject and across a population. Machine learning models are particularly suited to capture these patt...

Bibliográfalaš dieđut
Váldodahkkit:	Dinsdale, NK, Bluemke, E, Smith, SM, Arya, Z, Vidaurre, D, Jenkinson, M, Namburete, AIL
Materiálatiipa:	Journal article
Giella:	English
Almmustuhtton:	Elsevier 2020

Geahča maid

Deep learning-based unlearning of dataset bias for MRI harmonisation and confound removal.
Dahkki: Dinsdale, NK, et al.
Almmustuhtton: (2020)

Unlearning scanner bias for MRI harmonisation
Dahkki: Dinsdale, NK, et al.
Almmustuhtton: (2020)

Prototype learning for explainable brain age prediction
Dahkki: Hesse, LS, et al.
Almmustuhtton: (2024)

Unlearning scanner bias for MRI harmonisation in medical image segmentation
Dahkki: Dinsdale, NK, et al.
Almmustuhtton: (2020)

Challenges for machine learning in clinical translation of big data imaging studies
Dahkki: Dinsdale, NK, et al.
Almmustuhtton: (2022)

SFHarmony: source free domain adaptation for distributed neuroimaging analysis
Dahkki: Dinsdale, NK, et al.
Almmustuhtton: (2024)

STAMP: Simultaneous Training and Model Pruning for low data regimes in medical image segmentation
Dahkki: Dinsdale, NK, et al.
Almmustuhtton: (2022)

TEDS-Net: enforcing diffeomorphisms in spatial transformers to guarantee topology preservation in segmentations
Dahkki: Wyburd, MK, et al.
Almmustuhtton: (2021)

Anatomically plausible segmentations: explicitly preserving topology through prior deformations
Dahkki: Wyburd, MK, et al.
Almmustuhtton: (2024)

Optimising convolutional neural networks for large-scale neuroimaging studies
Dahkki: Dinsdale, NK
Almmustuhtton: (2021)

Self-supervised ultrasound to MRI fetal brain image synthesis
Dahkki: Jiao, J, et al.
Almmustuhtton: (2020)

Automated fetal brain extraction from clinical ultrasound volumes using 3D convolutional neural networks
Dahkki: Moser, F, et al.
Almmustuhtton: (2020)

The impact of transfer learning on 3D deep learning convolutional neural network segmentation of the hippocampus in mild cognitive impairment and Alzheimer disease subjects
Dahkki: Balboni, E, et al.
Almmustuhtton: (2022)

Subcortical segmentation of the fetal brain in 3D ultrasound using deep learning
Dahkki: Hesse, LS, et al.
Almmustuhtton: (2022)

Behavioural relevance of spontaneous, transient brain network interactions in fMRI
Dahkki: Vidaurre, D, et al.
Almmustuhtton: (2021)

Spatial warping network for 3D segmentation of the hippocampus in MR images
Dahkki: Dinsdale, N, et al.
Almmustuhtton: (2019)

Accurate brain age prediction with lightweight deep neural networks
Dahkki: Peng, H, et al.
Almmustuhtton: (2020)

Deep learning approaches to multimodal MRI brain age estimation
Dahkki: Roibu, A-C
Almmustuhtton: (2023)

BEAN: brain extraction and alignment network for 3D fetal neurosonography
Dahkki: Moser, F, et al.
Almmustuhtton: (2022)

Optimising network modelling methods for fMRI
Dahkki: Pervaiz, U, et al.
Almmustuhtton: (2020)

Optimising a simple fully convolutional network for accurate brain age prediction in the PAC 2019 challenge
Dahkki: Gong, W, et al.
Almmustuhtton: (2021)

Improving brain tumor segmentation in MRI images through enhanced convolutional neural networks
Dahkki: Ayomide, Kabirat Sulaiman, et al.
Almmustuhtton: (2023)

Anatomy-aware self-supervised fetal MRI synthesis from unpaired ultrasound images
Dahkki: Jiao, J, et al.
Almmustuhtton: (2019)

FedHarmony: unlearning scanner bias with distributed data
Dahkki: Dinsdale, N, et al.
Almmustuhtton: (2022)

Estimation of brain age delta from brain imaging.
Dahkki: Smith, S, et al.
Almmustuhtton: (2019)

Using deep convolutional neural networks to forecast spatial patterns of Amazonian deforestation
Dahkki: Ball, JGC, et al.
Almmustuhtton: (2022)

DeepLOB: Deep convolutional neural networks for limit order books
Dahkki: Zhang, Z, et al.
Almmustuhtton: (2019)

Deep convolutional inverse graphics network
Dahkki: Kohli, Pushmeet, et al.
Almmustuhtton: (2017)

Cortical plate segmentation using CNNs in 3D fetal ultrasound
Dahkki: Wyburd, MK, et al.
Almmustuhtton: (2020)

Multi-task CNN for structural semantic segmentation in 3D fetal brain ultrasound
Dahkki: Venturini, L, et al.
Almmustuhtton: (2020)

Age-based facial recognition using convoluted neural network deep learning algorithm
Dahkki: Julius Yong Wu Jien, et al.
Almmustuhtton: (2020)

Deep learning convolutional network for image classification
Dahkki: Moektijono, Isselin
Almmustuhtton: (2019)

Deep Convolutional Networks are Hierarchical Kernel Machines
Dahkki: Anselmi, Fabio, et al.
Almmustuhtton: (2015)

Hierarchically Local Tasks and Deep Convolutional Networks
Dahkki: Deza, Arturo, et al.
Almmustuhtton: (2020)

Approximating continuous convolutions for deep network compression
Dahkki: Costain, TW, et al.
Almmustuhtton: (2022)

Approximating continuous convolutions for deep network compression
Dahkki: Costain, TW, et al.
Almmustuhtton: (2022)

Deep insights into convolutional networks for video recognition
Dahkki: Feichtenhofer, C, et al.
Almmustuhtton: (2019)

Fully Hyperbolic Graph Convolutional Neural Networks for Age Prediction with Multi-Modal Brain Data
Dahkki: Ramirez, Hugo
Almmustuhtton: (2024)

Brain network dynamics are hierarchically organized in time
Dahkki: Vidaurre, D, et al.
Almmustuhtton: (2017)

Visual search and application using deep learning (Age group classification with convolution neural network)
Dahkki: Low, Benjamin
Almmustuhtton: (2018)