Search Results for author: Mike E. Davies

Found 25 papers, 9 papers with code

Adaptive Kernel Kalman Filter

no code implementations15 Mar 2022 Mengwei Sun, Mike E. Davies, Ian K. Proudler, James R. Hopgood

With this filter, the arbitrary predictive and posterior distributions of hidden states are approximated using the empirical kernel mean embeddings (KMEs) in reproducing kernel Hilbert spaces (RKHSs).

Sketched RT3D: How to reconstruct billions of photons per second

1 code implementation2 Mar 2022 Julián Tachella, Michael P. Sheehan, Mike E. Davies

Single-photon light detection and ranging (lidar) captures depth and intensity information of a 3D scene.

3D Reconstruction

Deep Unrolling for Magnetic Resonance Fingerprinting

no code implementations23 Jan 2022 Dongdong Chen, Mike E. Davies, Mohammad Golbabaee

Magnetic Resonance Fingerprinting (MRF) has emerged as a promising quantitative MR imaging approach.

Magnetic Resonance Fingerprinting

Robust Equivariant Imaging: a fully unsupervised framework for learning to image from noisy and partial measurements

1 code implementation CVPR 2022 Dongdong Chen, Julián Tachella, Mike E. Davies

Deep networks provide state-of-the-art performance in multiple imaging inverse problems ranging from medical imaging to computational photography.

Self-Supervised Learning

Compressive Independent Component Analysis: Theory and Algorithms

1 code implementation15 Oct 2021 Michael P. Sheehan, Mike E. Davies

Compressive learning forms the exciting intersection between compressed sensing and statistical learning where one exploits forms of sparsity and structure to reduce the memory and/or computational complexity of the learning task.

Surface Detection for Sketched Single Photon Lidar

1 code implementation14 May 2021 Michael P. Sheehan, Julián Tachella, Mike E. Davies

The computational load of the proposed detection algorithm depends solely on the size of the sketch, in contrast to previous algorithms that depend at least linearly in the number of collected photons or histogram bins, paving the way for fast, accurate and memory efficient lidar estimation.

Equivariant Imaging: Learning Beyond the Range Space

1 code implementation ICCV 2021 Dongdong Chen, Julián Tachella, Mike E. Davies

In various imaging problems, we only have access to compressed measurements of the underlying signals, hindering most learning-based strategies which usually require pairs of signals and associated measurements for training.

Image Inpainting

COIN: Contrastive Identifier Network for Breast Mass Diagnosis in Mammography

no code implementations29 Dec 2020 Heyi Li, Dongdong Chen, William H. Nailon, Mike E. Davies, David Laurenson

Computer-aided breast cancer diagnosis in mammography is a challenging problem, stemming from mammographical data scarcity and data entanglement.

Contrastive Learning

Dual Convolutional Neural Networks for Breast Mass Segmentation and Diagnosis in Mammography

no code implementations7 Aug 2020 Heyi Li, Dong-Dong Chen, William H. Nailon, Mike E. Davies, David Laurenson

In this paper, we introduce a novel deep learning framework for mammogram image processing, which computes mass segmentation and simultaneously predict diagnosis results.

Compressive MR Fingerprinting reconstruction with Neural Proximal Gradient iterations

1 code implementation27 Jun 2020 Dongdong Chen, Mike E. Davies, Mohammad Golbabaee

Consistency of the predictions with respect to the physical forward model is pivotal for reliably solving inverse problems.

De-aliasing Magnetic Resonance Fingerprinting

Compressive Learning for Semi-Parametric Models

no code implementations22 Oct 2019 Michael P. Sheehan, Antoine Gonon, Mike E. Davies

In the compressive learning theory, instead of solving a statistical learning problem from the input data, a so-called sketch is computed from the data prior to learning.

Learning Theory

Signed Laplacian Deep Learning with Adversarial Augmentation for Improved Mammography Diagnosis

no code implementations30 Jun 2019 Heyi Li, Dong-Dong Chen, William H. Nailon, Mike E. Davies, David I. Laurenson

Computer-aided breast cancer diagnosis in mammography is limited by inadequate data and the similarity between benign and cancerous masses.

A Deep DUAL-PATH Network for Improved Mammogram Image Processing

no code implementations1 Mar 2019 Heyi Li, Dong-Dong Chen, William H. Nailon, Mike E. Davies, Dave Laurenson

We present, for the first time, a novel deep neural network architecture called \dcn with a dual-path connection between the input image and output class label for mammogram image processing.

General Classification

CoverBLIP: scalable iterative matched filtering for MR Fingerprint recovery

no code implementations6 Sep 2018 Mohammad Golbabaee, Zhouye Chen, Yves Wiaux, Mike E. Davies

Current proposed solutions for the high dimensionality of the MRF reconstruction problem rely on a linear compression step to reduce the matching computations and boost the efficiency of fast but non-scalable searching schemes such as the KD-trees.

Balanced multi-shot EPI for accelerated Cartesian MRF: An alternative to spiral MRF

no code implementations6 Sep 2018 Arnold Julian Vinoj Benjamin, Pedro A. Gómez, Mohammad Golbabaee, Tim Sprenger, Marion I. Menzel, Mike E. Davies, Ian Marshall

The main purpose of this study is to show that a highly accelerated Cartesian MRF scheme using a multi-shot EPI readout (i. e. multi-shot EPI-MRF) can produce good quality multi-parametric maps such as T1, T2 and proton density (PD) in a sufficiently short scan duration that is similar to conventional MRF.

Geometry of Deep Learning for Magnetic Resonance Fingerprinting

no code implementations5 Sep 2018 Mohammad Golbabaee, Dong-Dong Chen, Pedro A. Gómez, Marion I. Menzel, Mike E. Davies

Current popular methods for Magnetic Resonance Fingerprint (MRF) recovery are bottlenecked by the heavy storage and computation requirements of a dictionary-matching (DM) step due to the growing size and complexity of the fingerprint dictionaries in multi-parametric quantitative MRI applications.

Dimensionality Reduction Image Reconstruction +1

Learning Discriminative Representation with Signed Laplacian Restricted Boltzmann Machine

no code implementations28 Aug 2018 Dongdong Chen, Jiancheng Lv, Mike E. Davies

We investigate the potential of a restricted Boltzmann Machine (RBM) for discriminative representation learning.

Representation Learning

Direct Estimation of Pharmacokinetic Parameters from DCE-MRI using Deep CNN with Forward Physical Model Loss

no code implementations8 Apr 2018 Cagdas Ulas, Giles Tetteh, Michael J. Thrippleton, Paul A. Armitage, Stephen D. Makin, Joanna M. Wardlaw, Mike E. Davies, Bjoern H. Menze

Dynamic contrast-enhanced (DCE) MRI is an evolving imaging technique that provides a quantitative measure of pharmacokinetic (PK) parameters in body tissues, in which series of T1-weighted images are collected following the administration of a paramagnetic contrast agent.

Time Series

Cover Tree Compressed Sensing for Fast MR Fingerprint Recovery

no code implementations23 Jun 2017 Mohammad Golbabaee, Zhouye Chen, Yves Wiaux, Mike E. Davies

We adopt data structure in the form of cover trees and iteratively apply approximate nearest neighbour (ANN) searches for fast compressed sensing reconstruction of signals living on discrete smooth manifolds.

Magnetic Resonance Fingerprinting

Sample Distortion for Compressed Imaging

no code implementations22 Mar 2013 Chunli Guo, Mike E. Davies

We propose the notion of a sample distortion (SD) function for independent and identically distributed (i. i. d) compressive distributions to fundamentally quantify the achievable reconstruction performance of compressed sensing for certain encoder-decoder pairs at a given sampling ratio.

Iterative Hard Thresholding for Compressed Sensing

1 code implementation5 May 2008 Thomas Blumensath, Mike E. Davies

- It requires a fixed number of iterations depending only on the logarithm of a form of signal to noise ratio of the signal.

Information Theory Numerical Analysis Information Theory Numerical Analysis

Cannot find the paper you are looking for? You can Submit a new open access paper.