57 results on '"Varsavsky, Thomas"'
Search Results
52. Multi-domain adaptation in brain MRI through paired consistency and adversarial learning
- Author
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Wang, Qian, Milletari, Fausto, Rieke, Nicola, Nguyen, Hien V., Roysam, Badri, Albarqouni, Shadi, Cardoso, M. Jorge, Xu, Ziyue, Kamnitsas, Konstantinos, Patel, Vishal, Jiang, Steve, Zhou, Kevin, Luu, Khoa, Le, Ngan, Orbes-Arteaga, Mauricio, Varsavsky, Thomas, Sudre, Carole H., Eaton-Rosen, Zach, Haddow, Lewis J., Sørensen, Lauge, Nielsen, Mads, Pai, Akshay, Ourselin, Sébastien, Modat, Marc, Nachev, Parashkev, Wang, Qian, Milletari, Fausto, Rieke, Nicola, Nguyen, Hien V., Roysam, Badri, Albarqouni, Shadi, Cardoso, M. Jorge, Xu, Ziyue, Kamnitsas, Konstantinos, Patel, Vishal, Jiang, Steve, Zhou, Kevin, Luu, Khoa, Le, Ngan, Orbes-Arteaga, Mauricio, Varsavsky, Thomas, Sudre, Carole H., Eaton-Rosen, Zach, Haddow, Lewis J., Sørensen, Lauge, Nielsen, Mads, Pai, Akshay, Ourselin, Sébastien, Modat, Marc, and Nachev, Parashkev
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Supervised learning algorithms trained on medical images will often fail to generalize across changes in acquisition parameters. Recent work in domain adaptation addresses this challenge and successfully leverages labeled data in a source domain to perform well on an unlabeled target domain. Inspired by recent work in semi-supervised learning we introduce a novel method to adapt from one source domain to n target domains (as long as there is paired data covering all domains). Our multi-domain adaptation method utilises a consistency loss combined with adversarial learning. We provide results on white matter lesion hyperintensity segmentation from brain MRIs using the MICCAI 2017 challenge data as the source domain and two target domains. The proposed method significantly outperforms other domain adaptation baselines.
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- 2019
53. Rapid implementation of mobile technology for real-time epidemiology of COVID-19.
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Drew, David A., Nguyen, Long H., Steves, Claire J., Menni, Cristina, Freydin, Maxim, Varsavsky, Thomas, Sudre, Carole H., Cardoso, M. Jorge, Ourselin, Sebastien, Wolf, Jonathan, Spector, Tim D., and Chan, Andrew T.
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- 2020
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54. Geo-social gradients in predicted COVID-19 prevalence in Great Britain: results from 1 960 242 users of the COVID-19 Symptoms Study app.
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Bowyer, Ruth C. E., Varsavsky, Thomas, Thompson, Ellen J., Sudre, Carole H., Murray, Benjamin A. K., Freidin, Maxim B., Yarand, Darioush, Ganesh, Sajaysurya, Capdevila, Joan, Bakker, Elco, Cardoso, M. Jorge, Davies, Richard, Wolf, Jonathan, Spector, Tim D., Ourselin, Sebastien, Steves, Claire J., and Menni, Cristina
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COVID-19 ,DISEASE mapping ,COVID-19 pandemic ,MEDICAL research ,BIOMEDICAL engineering - Abstract
Understanding the geographical distribution of COVID-19 through the general population is key to the provision of adequate healthcare services. Using self-reported data from 1 960 242 unique users in Great Britain (GB) of the COVID-19 Symptom Study app, we estimated that, concurrent to the GB government sanctioning lockdown, COVID-19 was distributed across GB, with evidence of 'urban hotspots'. We found a geo-social gradient associated with predicted disease prevalence suggesting urban areas and areas of higher deprivation are most affected. Our results demonstrate use of self-reported symptoms data to provide focus on geographical areas with identified risk factors. [ABSTRACT FROM AUTHOR]
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- 2021
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55. Cancer and Risk of COVID-19 Through a General Community Survey.
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Lee KA, Ma W, Sikavi DR, Drew DA, Nguyen LH, Bowyer RCE, Cardoso MJ, Fall T, Freidin MB, Gomez M, Graham M, Guo CG, Joshi AD, Kwon S, Lo CH, Lochlainn MN, Menni C, Murray B, Mehta R, Song M, Sudre CH, Bataille V, Varsavsky T, Visconti A, Franks PW, Wolf J, Steves CJ, Ourselin S, Spector TD, and Chan AT
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- Adult, Age Factors, Aged, COVID-19 diagnosis, COVID-19 immunology, COVID-19 virology, Female, Humans, Male, Middle Aged, Neoplasms complications, Neoplasms drug therapy, Neoplasms immunology, Retrospective Studies, Risk Factors, SARS-CoV-2 immunology, Sex Factors, Surveys and Questionnaires statistics & numerical data, Young Adult, Antineoplastic Agents adverse effects, COVID-19 epidemiology, COVID-19 Testing statistics & numerical data, Neoplasms epidemiology, SARS-CoV-2 isolation & purification
- Abstract
Individuals with cancer may be at high risk for coronavirus disease 2019 (COVID-19) and adverse outcomes. However, evidence from large population-based studies examining whether cancer and cancer-related therapy exacerbates the risk of COVID-19 infection is still limited. Data were collected from the COVID Symptom Study smartphone application since March 29 through May 8, 2020. Among 23,266 participants with cancer and 1,784,293 without cancer, we documented 10,404 reports of a positive COVID-19 test. Compared with participants without cancer, those living with cancer had a 60% increased risk of a positive COVID-19 test. Among patients with cancer, current treatment with chemotherapy or immunotherapy was associated with a 2.2-fold increased risk of a positive test. The association between cancer and COVID-19 infection was stronger among participants >65 years and males. Future studies are needed to identify subgroups by tumor types and treatment regimens who are particularly at risk for COVID-19 infection and adverse outcomes., (© 2020 The Authors. The Oncologist published by Wiley Periodicals LLC on behalf of AlphaMed Press.)
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- 2021
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56. Detecting COVID-19 infection hotspots in England using large-scale self-reported data from a mobile application: a prospective, observational study.
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Varsavsky T, Graham MS, Canas LS, Ganesh S, Pujol JC, Sudre CH, Murray B, Modat M, Cardoso MJ, Astley CM, Drew DA, Nguyen LH, Fall T, Gomez MF, Franks PW, Chan AT, Davies R, Wolf J, Steves CJ, Spector TD, and Ourselin S
- Abstract
Background: As many countries seek to slow the spread of COVID-19 without reimposing national restrictions, it has become important to track the disease at a local level to identify areas in need of targeted intervention., Methods: We performed modelling on longitudinal, self-reported data from users of the COVID Symptom Study app in England between 24 March and 29 September, 2020. Combining a symptom-based predictive model for COVID-19 positivity and RT-PCR tests provided by the Department of Health we were able to estimate disease incidence, prevalence and effective reproduction number. Geographically granular estimates were used to highlight regions with rapidly increasing case numbers, or hotspots., Findings: More than 2.8 million app users in England provided 120 million daily reports of their symptoms, and recorded the results of 170,000 PCR tests. On a national level our estimates of incidence and prevalence showed similar sensitivity to changes as two national community surveys: the ONS and REACT-1 studies. On 28 September 2020 we estimated 15,841 (95% CI 14,023-17,885) daily cases, a prevalence of 0.53% (95% CI 0.45-0.60), and R(t) of 1.17 (95% credible interval 1.15-1.19) in England. On a geographically granular level, on 28 September 2020 we detected 15 of the 20 regions with highest incidence according to Government test data, with indications that our method may be able to detect rapid case increases in regions where Government testing provision is more limited., Interpretation: Self-reported data from mobile applications can provide an agile resource to inform policymakers during a fast-moving pandemic, serving as an independent and complementary resource to more traditional instruments for disease surveillance., Funding: Zoe Global Limited, Department of Health, Wellcome Trust, EPSRC, NIHR, MRC, Alzheimer's Society.
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- 2020
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57. Risk of COVID-19 among frontline healthcare workers and the general community: a prospective cohort study.
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Nguyen LH, Drew DA, Joshi AD, Guo CG, Ma W, Mehta RS, Sikavi DR, Lo CH, Kwon S, Song M, Mucci LA, Stampfer MJ, Willett WC, Eliassen AH, Hart JE, Chavarro JE, Rich-Edwards JW, Davies R, Capdevila J, Lee KA, Lochlainn MN, Varsavsky T, Graham MS, Sudre CH, Cardoso MJ, Wolf J, Ourselin S, Steves CJ, Spector TD, and Chan AT
- Abstract
Background: Data for frontline healthcare workers (HCWs) and risk of SARS-CoV-2 infection are limited and whether personal protective equipment (PPE) mitigates this risk is unknown. We evaluated risk for COVID-19 among frontline HCWs compared to the general community and the influence of PPE., Methods: We performed a prospective cohort study of the general community, including frontline HCWs, who reported information through the COVID Symptom Study smartphone application beginning on March 24 (United Kingdom, U.K.) and March 29 (United States, U.S.) through April 23, 2020. We used Cox proportional hazards modeling to estimate multivariate-adjusted hazard ratios (aHRs) of a positive COVID-19 test., Findings: Among 2,035,395 community individuals and 99,795 frontline HCWs, we documented 5,545 incident reports of a positive COVID-19 test over 34,435,272 person-days. Compared with the general community, frontline HCWs had an aHR of 11·6 (95% CI: 10·9 to 12·3) for reporting a positive test. The corresponding aHR was 3·40 (95% CI: 3·37 to 3·43) using an inverse probability weighted Cox model adjusting for the likelihood of receiving a test. A symptom-based classifier of predicted COVID-19 yielded similar risk estimates. Compared with HCWs reporting adequate PPE, the aHRs for reporting a positive test were 1·46 (95% CI: 1·21 to 1·76) for those reporting PPE reuse and 1·31 (95% CI: 1·10 to 1·56) for reporting inadequate PPE. Compared with HCWs reporting adequate PPE who did not care for COVID-19 patients, HCWs caring for patients with documented COVID-19 had aHRs for a positive test of 4·83 (95% CI: 3·99 to 5·85) if they had adequate PPE, 5·06 (95% CI: 3·90 to 6·57) for reused PPE, and 5·91 (95% CI: 4·53 to 7·71) for inadequate PPE., Interpretation: Frontline HCWs had a significantly increased risk of COVID-19 infection, highest among HCWs who reused PPE or had inadequate access to PPE. However, adequate supplies of PPE did not completely mitigate high-risk exposures., Funding: Zoe Global Ltd., Wellcome Trust, EPSRC, NIHR, UK Research and Innovation, Alzheimer's Society, NIH, NIOSH, Massachusetts Consortium on Pathogen Readiness., Competing Interests: Declaration of interests JW, RD, and JC are employees of Zoe Global Ltd. TDS is a consultant to Zoe Global Ltd. DAD and ATC previously served as investigators on a clinical trial of diet and lifestyle using a separate mobile application that was supported by Zoe Global Ltd. Other authors have no conflict of interest to declare.
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- 2020
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