Abstract PS3-31: Training multi-professional mammogram readers to interpret abbreviated breast MRI (FAST MRI): A UK multi-centre study

Autor: Rebecca Geach, Sian Taylor-Phillips, Andrea Marshall, Sadie McKeown-Keegan, Claire Hulme, Elizabeth O'Flynn, Lyn Jones, Mark D. Halling-Brown, Janet A. Dunn, Hesam Ghiasvand, Sam A Harding, Premkumar Elangovan, Sarah Vinnicombe

Publikováno v: Cancer Research. 81:PS3-31

Background: As a breast cancer screening test, FAST MRI has the potential to combine the sensitivity of dynamic contrast enhanced breast MRI (DCE-MRI) with costs nearer to those of mammography. In the UK the NHS Breast Screening Programme (NHSBSP) sc

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::fe7fd132d8802e11fa53afddd6b4c617
https://doi.org/10.1158/1538-7445.sabcs20-ps3-31

Erratum to: An overview of the National COVID-19 Chest Imaging Database: data quality and cohort analysis

Autor: Mark D. Halling-Brown, Daniel Schofield, Rosalind Berka, Ottavia Bertolli, Joseph Jacob, Ashwin Chopra, Dominic Cushnan, Emily Jefferson, François Lemarchand, Jeremy C Wyatt, Alberto Favaro, Nccid Collaborative, Tara Ganepola, Gergely Imreh, Samie Dorgham, Oscar Bennett

Publikováno v: GigaScience

Background The National COVID-19 Chest Imaging Database (NCCID) is a centralized database containing mainly chest X-rays and computed tomography scans from patients across the UK. The objective of the initiative is to support a better understanding o

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::36ed7c3b801ed873398326888d6b3790
https://doi.org/10.1093/gigascience/giab083

Using imaging to combat a pandemic: rationale for developing the UK National COVID-19 Chest Imaging Database

Autor: Claire Bloomfield, Myles Kirby, Iain Buchan, R. McStay, Joseph Jacob, Daniel C. Alexander, Mark D. Halling-Brown, Annemarie B Docherty, Jonathan C L Rodrigues, Malcolm G Semple, Dominic Cushnan, Giles Roditi, J Kenneth Baillie, Rosalind Berka, Samanjit Hare, Neil J. Sebire, Nick Woznitza, G. Robinson, Peter J. M. Openshaw, James Blackwood, Geoff J M Parker, Annette Johnstone, Gerry Reilly, Emily Jefferson, Catherine Sudlow, Ottavia Bertolli, Anthony Edey, Arjun Nair, Indra Joshi, Fergus V. Gleeson, Alberto Favaro

Publikováno v: Jacob, J, Alexander, D, Baillie, J K, Berka, R, Bertolli, O, Blackwood, J, Buchan, I, Bloomfield, C, Cushnan, D, Docherty, A, Edey, A, Favaro, A, Gleeson, F, Halling-Brown, M, Hare, S, Jefferson, E, Johnstone, A, Kirby, M, Mcstay, R, Nair, A, Openshaw, P J M, Parker, G, Reilly, G, Robinson, G, Roditi, G, Rodrigues, J C L, Sebire, N, Semple, M G, Sudlow, C, Woznitza, N & Joshi, I 2020, ' Using imaging to combat a pandemic : rationale for developing the UK National COVID-19 Chest Imaging Database ', European Respiratory Journal . https://doi.org/10.1183/13993003.01809-2020
The European Respiratory Journal
article-version (AM) Accepted Manuscript

Since the emergence of the novel coronavirus SARS-CoV-2 in Wuhan, China in 2019 [1], the resulting COVID-19 disease has rapidly transitioned into a global pandemic with over 31 000 deaths and 215 000 infections in the UK as of 10th May 2020 [2]. Conf

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3ce5c26eac134a3e8b5effa6304dd72f
https://eprints.gla.ac.uk/220857/1/220857.pdf

Deep learning to calculate breast density from processed mammography images

Autor: Matthew Trumble, Kenneth C. Young, Louise Wilkinson, Lucy M. Warren, Rosalind M. Given-Wilson, David R. Dance, Sandra Gomes, Mark D. Halling-Brown, Peter Harris

Publikováno v: 15th International Workshop on Breast Imaging (IWBI2020).

Purpose: To calculate continuous breast density measures from processed images using deep learning. Method: Processed and unprocessed mammograms were collected for 3251 women attending the UK NHS Breast Screening Programme (NHSBSP). The breast densit

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::8a58392c7f3c1040cd0b0d4a4b998e92
https://doi.org/10.1117/12.2561278

Can Deep Learning Detect Esophageal Lesions In PET-CT Scans?

Autor: Emma Lewis, Rhodri L. Smith, Vineet Prakash, Ian Ackerley, James Scuffham, Kevin Wells, Emilio Spezi, Mark D. Halling-Brown

Publikováno v: 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC).

PET-CT scans using 18F-FDG with a co-registered CT scan are increasingly used to detect cancer. This paper compares deep learning-based lesion detection tools trained on PET, CT and combined modality data. 486 pre-contoured scans were used from a ret

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::3e952d67b63477cd75054838177c4ce2
https://doi.org/10.1109/nss/mic42101.2019.9059833