47 citations · 122 across the 16 of their papers we have counts for
7 papers · 1 filter
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…