8 research outputs found
Semi-Supervised Learning with Graphs: Covariance Based Superpixels for Hyperspectral Image Classification
In this paper, we present a graph-based semi-supervised framework for
hyperspectral image classification. We first introduce a novel superpixel
algorithm based on the spectral covariance matrix representation of pixels to
provide a better representation of our data. We then construct a superpixel
graph, based on carefully considered feature vectors, before performing
classification. We demonstrate, through a set of experimental results using two
benchmarking datasets, that our approach outperforms three state-of-the-art
classification frameworks, especially when an extremely small amount of
labelled data is used.Case Studentship with the NP
Multi-tasking to Correct: Motion-Compensated MRI via Joint Reconstruction and Registration
This work addresses a central topic in Magnetic Resonance Imaging (MRI) which
is the motion-correction problem in a joint reconstruction and registration
framework. From a set of multiple MR acquisitions corrupted by motion, we aim
at - jointly - reconstructing a single motion-free corrected image and
retrieving the physiological dynamics through the deformation maps. To this
purpose, we propose a novel variational model. First, we introduce an
fidelity term, which intertwines reconstruction and registration along with the
weighted total variation. Second, we introduce an additional regulariser which
is based on the hyperelasticity principles to allow large and smooth
deformations. We demonstrate through numerical results that this combination
creates synergies in our complex variational approach resulting in higher
quality reconstructions and a good estimate of the breathing dynamics. We also
show that our joint model outperforms in terms of contrast, detail and blurring
artefacts, a sequential approach.Cambridge Cancer Centre, CMIH and CCIMI, University of Cambridge
Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
Abstract: Machine learning methods offer great promise for fast and accurate detection and prognostication of coronavirus disease 2019 (COVID-19) from standard-of-care chest radiographs (CXR) and chest computed tomography (CT) images. Many articles have been published in 2020 describing new machine learning-based models for both of these tasks, but it is unclear which are of potential clinical utility. In this systematic review, we consider all published papers and preprints, for the period from 1 January 2020 to 3 October 2020, which describe new machine learning models for the diagnosis or prognosis of COVID-19 from CXR or CT images. All manuscripts uploaded to bioRxiv, medRxiv and arXiv along with all entries in EMBASE and MEDLINE in this timeframe are considered. Our search identified 2,212 studies, of which 415 were included after initial screening and, after quality screening, 62 studies were included in this systematic review. Our review finds that none of the models identified are of potential clinical use due to methodological flaws and/or underlying biases. This is a major weakness, given the urgency with which validated COVID-19 models are needed. To address this, we give many recommendations which, if followed, will solve these issues and lead to higher-quality model development and well-documented manuscripts
A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)
Meeting abstrac
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Semi-Supervised Learning with Graphs: Covariance Based Superpixels for Hyperspectral Image Classification
In this paper, we present a graph-based semi-supervised framework for
hyperspectral image classification. We first introduce a novel superpixel
algorithm based on the spectral covariance matrix representation of pixels to
provide a better representation of our data. We then construct a superpixel
graph, based on carefully considered feature vectors, before performing
classification. We demonstrate, through a set of experimental results using two
benchmarking datasets, that our approach outperforms three state-of-the-art
classification frameworks, especially when an extremely small amount of
labelled data is used.Case Studentship with the NP
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Multi-modal Hypergraph Diffusion Network with Dual Prior for Alzheimer Classification
The automatic early diagnosis of prodromal stages of Alzheimer’s disease is of great relevance for patient treatment to improve quality of life. We address this problem as a multi-modal classification task. Multi-modal data provides richer and complementary information. However, existing techniques only consider lower order relations between the data and single/multi-modal imaging data. In this work, we introduce a novel semi-supervised hypergraph learning framework for Alzheimer’s disease diagnosis. Our framework allows for higher-order relations among multi-modal imaging and non-imaging data whilst requiring a tiny labelled set. Firstly, we introduce a dual embedding strategy for constructing a robust hypergraph that preserves the data semantics. We achieve this by enforcing perturbation invariance at the image and graph levels using a contrastive based mechanism. Secondly, we present a dynamically adjusted hypergraph diffusion model, via a semi-explicit flow, to improve the predictive uncertainty. We demonstrate, through our experiments, that our framework is able to outperform current techniques for Alzheimer’s disease diagnosis
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Multi-tasking to Correct: Motion-Compensated MRI via Joint Reconstruction and Registration
© 2019, Springer Nature Switzerland AG. This work addresses a central topic in Magnetic Resonance Imaging (MRI) which is the motion-correction problem in a joint reconstruction and registration framework. From a set of multiple MR acquisitions corrupted by motion, we aim at - jointly - reconstructing a single motion-free corrected image and retrieving the physiological dynamics through the deformation maps. To this purpose, we propose a novel variational model. First, we introduce an L2 fidelity term, which intertwines reconstruction and registration along with the weighted total variation. Second, we introduce an additional regulariser which is based on the hyperelasticity principles to allow large and smooth deformations. We demonstrate through numerical results that this combination creates synergies in our complex variational approach resulting in higher quality reconstructions and a good estimate of the breathing dynamics. We also show that our joint model outperforms in terms of contrast, detail and blurring artefacts, a sequential approach.Cambridge Cancer Centre, CMIH and CCIMI, University of Cambridge
Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
Machine learning methods offer great promise for fast and accurate detection
and prognostication of COVID-19 from standard-of-care chest radiographs (CXR)
and computed tomography (CT) images. Many articles have been published in 2020
describing new machine learning-based models for both of these tasks, but it is
unclear which are of potential clinical utility. In this systematic review, we
search EMBASE via OVID, MEDLINE via PubMed, bioRxiv, medRxiv and arXiv for
published papers and preprints uploaded from January 1, 2020 to October 3, 2020
which describe new machine learning models for the diagnosis or prognosis of
COVID-19 from CXR or CT images. Our search identified 2,212 studies, of which
415 were included after initial screening and, after quality screening, 61
studies were included in this systematic review. Our review finds that none of
the models identified are of potential clinical use due to methodological flaws
and/or underlying biases. This is a major weakness, given the urgency with
which validated COVID-19 models are needed. To address this, we give many
recommendations which, if followed, will solve these issues and lead to higher
quality model development and well documented manuscripts