Graph-based deep learning for medical diagnosis and analysis: past, present and future
With the advances of data-driven machine learning research, a wide variety of prediction
problems have been tackled. It has become critical to explore how machine learning and …
problems have been tackled. It has become critical to explore how machine learning and …
Graph convolutional networks for multi-modality medical imaging: Methods, architectures, and clinical applications
Image-based characterization and disease understanding involve integrative analysis of
morphological, spatial, and topological information across biological scales. The …
morphological, spatial, and topological information across biological scales. The …
Training data distribution significantly impacts the estimation of tissue microstructure with machine learning
Purpose Supervised machine learning (ML) provides a compelling alternative to traditional
model fitting for parameter mapping in quantitative MRI. The aim of this work is to …
model fitting for parameter mapping in quantitative MRI. The aim of this work is to …
A microstructure estimation Transformer inspired by sparse representation for diffusion MRI
Diffusion magnetic resonance imaging (dMRI) is an important tool in characterizing tissue
microstructure based on biophysical models, which are typically multi-compartmental …
microstructure based on biophysical models, which are typically multi-compartmental …
Hybrid graph transformer for tissue microstructure estimation with undersampled diffusion MRI data
Advanced contemporary diffusion models for tissue microstructure often require diffusion
MRI (DMRI) data with sufficiently dense sampling in the diffusion wavevector space for …
MRI (DMRI) data with sufficiently dense sampling in the diffusion wavevector space for …
Mesh-based graph convolutional neural networks for modeling materials with microstructure
Predicting the evolution of a representative sample of a material with microstructure is a
fundamental problem in homogenization. In this work we propose a graph convolutional …
fundamental problem in homogenization. In this work we propose a graph convolutional …
[HTML][HTML] Optimisation of quantitative brain diffusion-relaxation MRI acquisition protocols with physics-informed machine learning
Diffusion-relaxation MRI aims to extract quantitative measures that characterise
microstructural tissue properties such as orientation, size, and shape, but long acquisition …
microstructural tissue properties such as orientation, size, and shape, but long acquisition …
Multimodal super-resolved q-space deep learning
Super-resolved q-space deep learning (SR-q-DL) has been developed to estimate high-
resolution (HR) tissue microstructure maps from low-quality diffusion magnetic resonance …
resolution (HR) tissue microstructure maps from low-quality diffusion magnetic resonance …
Angular super-resolution in diffusion MRI with a 3D recurrent convolutional autoencoder
M Lyon, P Armitage, MA Álvarez - … Conference on Medical …, 2022 - proceedings.mlr.press
High resolution diffusion MRI (dMRI) data is often constrained by limited scanning time in
clinical settings, thus restricting the use of downstream analysis techniques that would …
clinical settings, thus restricting the use of downstream analysis techniques that would …
How can spherical CNNs benefit ML-based diffusion MRI parameter estimation?
T Goodwin-Allcock, J McEwen, R Gray… - International Workshop …, 2022 - Springer
This paper demonstrates spherical convolutional neural networks (S-CNN) offer distinct
advantages over conventional fully-connected networks (FCN) at estimating scalar …
advantages over conventional fully-connected networks (FCN) at estimating scalar …