200 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of EdinburghGB12 papers
- Heriot-Watt UniversityGB4 papers
- Cedars-Sinai Medical CenterUS3 papers
- Centre for Inflammation ResearchGB3 papers
- Imperial College LondonGB2 papers
- Maxwell Institute for Mathematical SciencesGB2 papers
- Roslin InstituteGB2 papers
- Royal Hospital for Sick ChildrenGB2 papers
- The Alan Turing InstituteGB2 papers
- University of BathGB2 papers
- British Heart Foundation Centre for Cardiovascular ScienceGB1 paper
- Copenhagen University HospitalDK1 paper
12 papers
Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantification
Ben Philps, Maria del C. Valdes Hernandez, Chen Qin +8
White Matter Hyperintensities (WMH) are key neuroradiological markers of small vessel disease present in brain MRI. Assessment of WMH is important in research and clinics. However,…
Preictal Period Optimization for Deep Learning-Based Epileptic Seizure Prediction
Petros Koutsouvelis, Bartlomiej Chybowski, Alfredo Gonzalez-Sulser +2
Accurate prediction of epileptic seizures could prove critical for improving patient safety and quality of life in drug-resistant epilepsy. Although deep learning-based approaches…
Face Coverings, Aerosol Dispersion and Mitigation of Virus Transmission Risk
I. M. Viola, B. Peterson, G. Pisetta +17
The SARS-CoV-2 virus is primarily transmitted through virus-laden fluid particles ejected from the mouth of infected people. Face covers can mitigate the risk of virus transmission…
Disentangle, align and fuse for multimodal and semi-supervised image segmentation
Agisilaos Chartsias, Giorgos Papanastasiou, Chengjia Wang +4
Magnetic resonance (MR) protocols rely on several sequences to assess pathology and organ status properly. Despite advances in image analysis, we tend to treat each sequence, here…
FIRE: Unsupervised bi-directional inter-modality registration using deep networks
Chengjia Wang, Giorgos Papanastasiou, Agisilaos Chartsias +3
Inter-modality image registration is an critical preprocessing step for many applications within the routine clinical pathway. This paper presents an unsupervised deep inter-modali…
Disentangled Representation Learning in Cardiac Image Analysis
Agisilaos Chartsias, Thomas Joyce, Giorgos Papanastasiou +4
Typically, a medical image offers spatial information on the anatomy (and pathology) modulated by imaging specific characteristics. Many imaging modalities including Magnetic Reson…
Compressive optical imaging with a photonic lantern
Debaditya Choudhury, Duncan K. McNicholl, Audrey RepettI +4
The thin and flexible nature of optical fibres often makes them the ideal technology to view biological processes in-vivo, but current microendoscopic approaches are limited in spa…
Image computing for fibre-bundle endomicroscopy: A review
Antonios Perperidis, Kevin Dhaliwal, Stephen McLaughlin +1
Endomicroscopy is an emerging imaging modality, that facilitates the acquisition of in vivo, in situ optical biopsies, assisting diagnostic and potentially therapeutic intervention…
High fidelity fibre-based physiological sensing deep in tissue
Tushar R. Choudhary, Michael G. Tanner, Alicia Megia-Fernandez +13
Physiological sensing deep in tissue, remains a clinical challenge. Here a flexible miniaturised sensing optrode providing a platform to perform minimally invasive in vivo in situ…
Factorised spatial representation learning: application in semi-supervised myocardial segmentation
Agisilaos Chartsias, Thomas Joyce, Giorgos Papanastasiou +5
The success and generalisation of deep learning algorithms heavily depend on learning good feature representations. In medical imaging this entails representing anatomical informat…
Ovarian volume throughout life: a validated normative model
Thomas W. Kelsey, Sarah K. Dodwell, A. Graham Wilkinson +4
The measurement of ovarian volume has been shown to be a useful indirect indicator of the ovarian reserve in women of reproductive age, in the diagnosis and management of a number…
Digital clocks: simple Boolean models can quantitatively describe circadian systems
Ozgur Akman, Steven Watterson, Andrew Parton +3
The gene networks that comprise the circadian clock modulate biological function across a range of scales, from gene expression to performance and adaptive behaviour. The clock fun…