47 citations · 122 across the 16 of their papers we have counts for
18 papers
Low-field magnetic resonance image enhancement via stochastic image quality transfer
Hongxiang Lin, Matteo Figini, Felice D'Arco +10
Low-field (<1T) magnetic resonance imaging (MRI) scanners remain in widespread use in low- and middle-income countries (LMICs) and are commonly used for some applications in higher…
Deformably-Scaled Transposed Convolution
Stefano B. Blumberg, Daniele Raví, Mou-Cheng Xu +3
Transposed convolution is crucial for generating high-resolution outputs, yet has received little attention compared to convolution layers. In this work we revisit transposed convo…
Fitting a Directional Microstructure Model to Diffusion-Relaxation MRI Data with Self-Supervised Machine Learning
Jason P. Lim, Stefano B. Blumberg, Neil Narayan +4
Machine learning is a powerful approach for fitting microstructural models to diffusion MRI data. Early machine learning microstructure imaging implementations trained regressors t…
Experimental Design for Multi-Channel Imaging via Task-Driven Feature Selection
Stefano B. Blumberg, Paddy J. Slator, Daniel C. Alexander
This paper presents a data-driven, task-specific paradigm for experimental design, to shorten acquisition time, reduce costs, and accelerate the deployment of imaging devices. Curr…
A Game-Theoretic Framework for Managing Risk in Multi-Agent Systems
Oliver Slumbers, David Henry Mguni, Stephen Marcus McAleer +3
In order for agents in multi-agent systems (MAS) to be safe, they need to take into account the risks posed by the actions of other agents. However, the dominant paradigm in game t…
Learning Morphological Feature Perturbations for Calibrated Semi-Supervised Segmentation
Mou-Cheng Xu, Yu-Kun Zhou, Chen Jin +6
We propose MisMatch, a novel consistency-driven semi-supervised segmentation framework which produces predictions that are invariant to learnt feature perturbations. MisMatch consi…
VAFO-Loss: VAscular Feature Optimised Loss Function for Retinal Artery/Vein Segmentation
Yukun Zhou, Moucheng Xu, Yipeng Hu +5
Estimating clinically-relevant vascular features following vessel segmentation is a standard pipeline for retinal vessel analysis, which provides potential ocular biomarkers for bo…
Progressive Subsampling for Oversampled Data - Application to Quantitative MRI
Stefano B. Blumberg, Hongxiang Lin, Francesco Grussu +3
We present PROSUB: PROgressive SUBsampling, a deep learning based, automated methodology that subsamples an oversampled data set (e.g. multi-channeled 3D images) with minimal loss…
DeepReg: a deep learning toolkit for medical image registration
Yunguan Fu, Nina Montaña Brown, Shaheer U. Saeed +14
DeepReg (https://github.com/DeepRegNet/DeepReg) is a community-supported open-source toolkit for research and education in medical image registration using deep learning.
QuantNet: Transferring Learning Across Systematic Trading Strategies
Adriano Koshiyama, Sebastian Flennerhag, Stefano B. Blumberg +2
Systematic financial trading strategies account for over 80% of trade volume in equities and a large chunk of the foreign exchange market. In spite of the availability of data from…
Image Quality Transfer Enhances Contrast and Resolution of Low-Field Brain MRI in African Paediatric Epilepsy Patients
Matteo Figini, Hongxiang Lin, Godwin Ogbole +10
1.5T or 3T scanners are the current standard for clinical MRI, but low-field (<1T) scanners are still common in many lower- and middle-income countries for reasons of cost and robu…
Degenerative Adversarial NeuroImage Nets for Brain Scan Simulations: Application in Ageing and Dementia
Daniele Ravi, Stefano B. Blumberg, Silvia Ingala +3
Accurate and realistic simulation of high-dimensional medical images has become an important research area relevant to many AI-enabled healthcare applications. However, current sta…
Deep Learning for Low-Field to High-Field MR: Image Quality Transfer with Probabilistic Decimation Simulator
Hongxiang Lin, Matteo Figini, Ryutaro Tanno +10
MR images scanned at low magnetic field (T) have lower resolution in the slice direction and lower contrast, due to a relatively small signal-to-noise ratio (SNR) than those fr…
Multi-Stage Prediction Networks for Data Harmonization
Stefano B. Blumberg, Marco Palombo, Can Son Khoo +3
In this paper, we introduce multi-task learning (MTL) to data harmonization (DH); where we aim to harmonize images across different acquisition platforms and sites. This allows us…
ABCD Neurocognitive Prediction Challenge 2019: Predicting individual residual fluid intelligence scores from cortical grey matter morphology
Neil P. Oxtoby, Fabio S. Ferreira, Agoston Mihalik +12
We predicted residual fluid intelligence scores from T1-weighted MRI data available as part of the ABCD NP Challenge 2019, using morphological similarity of grey-matter regions acr…
ABCD Neurocognitive Prediction Challenge 2019: Predicting individual fluid intelligence scores from structural MRI using probabilistic segmentation and kernel ridge regression
Agoston Mihalik, Mikael Brudfors, Maria Robu +12
We applied several regression and deep learning methods to predict fluid intelligence scores from T1-weighted MRI scans as part of the ABCD Neurocognitive Prediction Challenge (ABC…
Disease Knowledge Transfer across Neurodegenerative Diseases
Razvan V. Marinescu, Marco Lorenzi, Stefano B. Blumberg +8
We introduce Disease Knowledge Transfer (DKT), a novel technique for transferring biomarker information between related neurodegenerative diseases. DKT infers robust multimodal bio…
Deeper Image Quality Transfer: Training Low-Memory Neural Networks for 3D Images
Stefano B. Blumberg, Ryutaro Tanno, Iasonas Kokkinos +1
In this paper we address the memory demands that come with the processing of 3-dimensional, high-resolution, multi-channeled medical images in deep learning. We exploit memory-effi…