A Hybrid Architecture (CO-CONNECT) to Facilitate Rapid Discovery and Access to Data Across the United Kingdom in Response to the COVID-19 Pandemic: Development Study

Autor: Emily Jefferson, Christian Cole, Shahzad Mumtaz, Samuel Cox, Thomas Charles Giles, Sam Adejumo, Esmond Urwin, Daniel Lea, Calum Macdonald, Joseph Best, Erum Masood, Gordon Milligan, Jenny Johnston, Scott Horban, Ipek Birced, Christopher Hall, Aaron S Jackson, Clare Collins, Sam Rising, Charlotte Dodsley, Jill Hampton, Andrew Hadfield, Roberto Santos, Simon Tarr, Vasiliki Panagi, Joseph Lavagna, Tracy Jackson, Antony Chuter, Jillian Beggs, Magdalena Martinez-Queipo, Helen Ward, Julie von Ziegenweidt, Frances Burns, Joanne Martin, Neil Sebire, Carole Morris, Declan Bradley, Rob Baxter, Anni Ahonen-Bishopp, Paul Smith, Amelia Shoemark, Ana M Valdes, Benjamin Ollivere, Charlotte Manisty, David Eyre, Stephanie Gallant, George Joy, Andrew McAuley, David Connell, Kate Northstone, Katie Jeffery, Emanuele Di Angelantonio, Amy McMahon, Mat Walker, Malcolm Gracie Semple, Jessica Mai Sims, Emma Lawrence, Bethan Davies, John Kenneth Baillie, Ming Tang, Gary Leeming, Linda Power, Thomas Breeze, Duncan Murray, Chris Orton, Iain Pierce, Ian Hall, Shamez Ladhani, Natalie Gillson, Matthew Whitaker, Laura Shallcross, David Seymour, Susheel Varma, Gerry Reilly, Andrew Morris, Susan Hopkins, Aziz Sheikh, Philip Quinlan

Publikováno v: Journal of Medical Internet Research, Vol 24, Iss 12, p e40035 (2022)

BackgroundCOVID-19 data have been generated across the United Kingdom as a by-product of clinical care and public health provision, as well as numerous bespoke and repurposed research endeavors. Analysis of these data has underpinned the United Kingd

Externí odkaz: https://doaj.org/article/54ca7e259fa5437b93518def8143ad8f

GANana: Unsupervised Domain Adaptation for Volumetric Regression of Fruit

Autor: Aaron S. Jackson, Zane K. J. Hartley, Michael P. Pound, Andrew P. French

Publikováno v: Plant Phenomics, Vol 2021 (2021)
Plant Phenomics

3D reconstruction of fruit is important as a key component of fruit grading and an important part of many size estimation pipelines. Like many computer vision challenges, the 3D reconstruction task suffers from a lack of readily available training da

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b618908e2ad99ae8f807d76f25d1f3af
https://doaj.org/article/cd6962c5566e4410975d5eb721851584

Deep Hourglass for Brain Tumor Segmentation

Autor: Eze Benson, Andrew P. French, Aaron S. Jackson, Tony P. Pridmore, Michael P. Pound

Publikováno v: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries ISBN: 9783030117252
BrainLes@MICCAI (2)

The segmentation of a brain tumour in an MRI scan is a challenging task, in this paper we present our results for this problem via the BraTS 2018 challenge, consisting of 210 high grade glioma (HGG) and 75 low grade glioma (LGG) volumes for training.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::63c7559751ce8adc6e254f9ee3db31ce
https://doi.org/10.1007/978-3-030-11726-9_37

Erratum to: Deep machine learning provides state-of-the-art performance in image-based plant phenotyping

Autor: Darren M. Wells, Erik H. Murchie, Andrew P. French, Adrian Bulat, Michael Wilson, Marcus Griffiths, Tony P. Pridmore, Georgios Tzimiropoulos, Alexandra J. Townsend, Michael P. Pound, Jonathan A. Atkinson, Aaron S. Jackson

Publikováno v: GigaScience

In plant phenotyping, it has become important to be able to measure many features on large image sets in order to aid genetic discovery. The size of the datasets, now often captured robotically, often precludes manual inspection, hence the motivation

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b3ff681a9cd35fab410c8b4b5904b5d5
http://europepmc.org/articles/PMC6055546

Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression

Autor: Georgios Tzimiropoulos, Aaron S. Jackson, Vasileios Argyriou, Adrian Bulat

Publikováno v: ICCV
ICCV 2017 : International Conference on Computer Vision

3D face reconstruction is a fundamental Computer Vision problem of extraordinary difficulty. Current systems often assume the availability of multiple facial images (sometimes from the same subject) as input, and must address a number of methodologic

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f42bb25c327de11cd8699517c907fbb7