Search Results for author: Stefano B. Blumberg

Found 15 papers, 8 papers with code

Low-field magnetic resonance image enhancement via stochastic image quality transfer

1 code implementation26 Apr 2023 Hongxiang Lin, Matteo Figini, Felice D'Arco, Godwin Ogbole, Ryutaro Tanno, Stefano B. Blumberg, Lisa Ronan, Biobele J. Brown, David W. Carmichael, Ikeoluwa Lagunju, Judith Helen Cross, Delmiro Fernandez-Reyes, Daniel C. Alexander

Here, we present Image Quality Transfer (IQT) to enhance low-field structural MRI by estimating from a low-field image the image we would have obtained from the same subject at high field.

Image Enhancement

Deformably-Scaled Transposed Convolution

no code implementations17 Oct 2022 Stefano B. Blumberg, Daniele Raví, Mou-Cheng Xu, Matteo Figini, Iasonas Kokkinos, Daniel C. Alexander

Transposed convolution is crucial for generating high-resolution outputs, yet has received little attention compared to convolution layers.

Image Enhancement Instance Segmentation +3

Experimental Design for Multi-Channel Imaging via Task-Driven Feature Selection

1 code implementation13 Oct 2022 Stefano B. Blumberg, Paddy J. Slator, Daniel C. Alexander

Here we present a new paradigm for experimental design that simultaneously optimizes the design (set of image channels) and trains a machine-learning model to execute a user-specified image-analysis task.

Experimental Design feature selection

A Game-Theoretic Framework for Managing Risk in Multi-Agent Systems

no code implementations30 May 2022 Oliver Slumbers, David Henry Mguni, Stephen Marcus McAleer, Stefano B. Blumberg, Jun Wang, Yaodong Yang

Although there are equilibrium concepts in game theory that take into account risk aversion, they either assume that agents are risk-neutral with respect to the uncertainty caused by the actions of other agents, or they are not guaranteed to exist.

Autonomous Driving Multi-agent Reinforcement Learning

Progressive Subsampling for Oversampled Data -- Application to Quantitative MRI

1 code implementation17 Mar 2022 Stefano B. Blumberg, Hongxiang Lin, Francesco Grussu, Yukun Zhou, Matteo Figini, Daniel C. Alexander

We build upon a recent dual-network approach that won the MICCAI MUlti-DIffusion (MUDI) quantitative MRI measurement sampling-reconstruction challenge, but suffers from deep learning training instability, by subsampling with a hard decision boundary.

Neural Architecture Search

VAFO-Loss: VAscular Feature Optimised Loss Function for Retinal Artery/Vein Segmentation

no code implementations12 Mar 2022 Yukun Zhou, MouCheng Xu, Yipeng Hu, Stefano B. Blumberg, An Zhao, Siegfried K. Wagner, Pearse A. Keane, Daniel C. Alexander

Estimating clinically-relevant vascular features following vessel segmentation is a standard pipeline for retinal vessel analysis, which provides potential ocular biomarkers for both ophthalmic disease and systemic disease.

Segmentation

QuantNet: Transferring Learning Across Systematic Trading Strategies

2 code implementations7 Apr 2020 Adriano Koshiyama, Sebastian Flennerhag, Stefano B. Blumberg, Nick Firoozye, Philip Treleaven

The encoder transforms market-specific data into an abstract latent representation that is processed by a global model shared by all markets, while the decoder learns a market-specific trading strategy based on both local and global information from the market-specific encoder and the global model.

Meta-Learning Transfer Learning

Image Quality Transfer Enhances Contrast and Resolution of Low-Field Brain MRI in African Paediatric Epilepsy Patients

no code implementations16 Mar 2020 Matteo Figini, Hongxiang Lin, Godwin Ogbole, Felice D Arco, Stefano B. Blumberg, David W. Carmichael, Ryutaro Tanno, Enrico Kaden, Biobele J. Brown, Ikeoluwa Lagunju, Helen J. Cross, Delmiro Fernandez-Reyes, Daniel C. Alexander

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 robustness to power failures.

Management

Degenerative Adversarial NeuroImage Nets for Brain Scan Simulations: Application in Ageing and Dementia

no code implementations3 Dec 2019 Daniele Ravi, Stefano B. Blumberg, Silvia Ingala, Frederik Barkhof, Daniel C. Alexander, Neil P. Oxtoby

To evaluate our approach, we trained the framework on 9852 T1-weighted MRI scans from 876 participants in the Alzheimer's Disease Neuroimaging Initiative dataset and held out a separate test set of 1283 MRI scans from 170 participants for quantitative and qualitative assessment of the personalised time series of synthetic images.

Image Quality Assessment Super-Resolution +3

Multi-Stage Prediction Networks for Data Harmonization

no code implementations26 Jul 2019 Stefano B. Blumberg, Marco Palombo, Can Son Khoo, Chantal M. W. Tax, Ryutaro Tanno, Daniel C. Alexander

Specifically, we introduce the Multi Stage Prediction (MSP) Network, a MTL framework that incorporates neural networks of potentially disparate architectures, trained for different individual acquisition platforms, into a larger architecture that is refined in unison.

Multi-Task Learning

Disease Knowledge Transfer across Neurodegenerative Diseases

2 code implementations11 Jan 2019 Razvan V. Marinescu, Marco Lorenzi, Stefano B. Blumberg, Alexandra L. Young, Pere P. Morell, Neil P. Oxtoby, Arman Eshaghi, Keir X. Yong, Sebastian J. Crutch, Polina Golland, Daniel C. Alexander

DKT infers robust multimodal biomarker trajectories in rare neurodegenerative diseases even when only limited, unimodal data is available, by transferring information from larger multimodal datasets from common neurodegenerative diseases.

Transfer Learning

Deeper Image Quality Transfer: Training Low-Memory Neural Networks for 3D Images

1 code implementation16 Aug 2018 Stefano B. Blumberg, Ryutaro Tanno, Iasonas Kokkinos, Daniel C. Alexander

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.

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