Sudre, Carole H.

Generalised Dice Overlap as a Deep Learning Loss Function for Highly Unbalanced Segmentations

Sudre, C. H., Li, W., Vercauteren, T., Ourselin, S., & Jorge Cardoso, M. (2017) · Lecture Notes in Computer Science

Medical Image Segmentation TechniquesAdvanced Neural Network Applications

Cited by 2,829

Attributes and predictors of long COVID

Sudre, C. H., Murray, B., Varsavsky, T., Graham, M. S., Penfold, R. S., Bowyer, R. C., Pujol, J. C., Klaser, K., Antonelli, M., Canas, L. S., et al. (2021) · Nature Medicine

Long-Term Effects of COVID-19COVID-19 Clinical Research Studies

Cited by 2,707

Real-time tracking of self-reported symptoms to predict potential COVID-19

Menni, C., Valdes, A. M., Freidin, M. B., Sudre, C. H., Nguyen, L. H., Drew, D. A., Ganesh, S., Varsavsky, T., Cardoso, M. J., El-Sayed Moustafa, J. S., et al. (2020) · Nature Medicine

COVID-19 Digital Contact TracingData-Driven Disease Surveillance

Cited by 1,517

Vaccine side-effects and SARS-CoV-2 infection after vaccination in users of the COVID Symptom Study app in the UK: a prospective observational study

Menni, C., Klaser, K., May, A., Polidori, L., Capdevila, J., Louca, P., Sudre, C. H., Nguyen, L. H., Drew, D. A., Merino, J., et al. (2021) · The Lancet Infectious Diseases

SARS-CoV-2 and COVID-19 ResearchVaccine Coverage and Hesitancy

Cited by 1,031

Risk factors and disease profile of post-vaccination SARS-CoV-2 infection in UK users of the COVID Symptom Study app: a prospective, community-based, nested, case-control study

Antonelli, M., Penfold, R. S., Merino, J., Sudre, C. H., Molteni, E., Berry, S., Canas, L. S., Graham, M. S., Klaser, K., Modat, M., et al. (2022) · The Lancet Infectious Diseases

SARS-CoV-2 and COVID-19 ResearchCOVID-19 epidemiological studies

Cited by 843

NiftyNet: a deep-learning platform for medical imaging

Gibson, E., Li, W., Sudre, C., Fidon, L., Shakir, D. I., Wang, G., Eaton-Rosen, Z., Gray, R., Doel, T., Hu, Y., et al. (2018) · Computer Methods and Programs in Biomedicine

Medical Image Segmentation TechniquesArtificial Intelligence in Healthcare and Education

Cited by 552

Rapid implementation of mobile technology for real-time epidemiology of COVID-19

Drew, D. A., Nguyen, L. H., Steves, C. J., Menni, C., Freydin, M., Varsavsky, T., Sudre, C. H., Cardoso, M. J., Ourselin, S., Wolf, J., et al. (2020) · Science

Data-Driven Disease SurveillanceCOVID-19 Digital Contact Tracing

Cited by 389

Changes in symptomatology, reinfection, and transmissibility associated with the SARS-CoV-2 variant B.1.1.7: an ecological study

Graham, M. S., Sudre, C. H., May, A., Antonelli, M., Murray, B., Varsavsky, T., Kläser, K., Canas, L. S., Molteni, E., Modat, M., et al. (2021) · The Lancet Public Health

SARS-CoV-2 and COVID-19 ResearchCOVID-19 epidemiological studies

Cited by 304

Associations between blood pressure across adulthood and late-life brain structure and pathology in the neuroscience substudy of the 1946 British birth cohort (Insight 46): an epidemiological study

Lane, C. A., Barnes, J., Nicholas, J. M., Sudre, C. H., Cash, D. M., Parker, T. D., Malone, I. B., Lu, K., James, S.-N., Keshavan, A., et al. (2019) · The Lancet Neurology

Dementia and Cognitive Impairment ResearchBlood Pressure and Hypertension Studies

Cited by 247

Understanding metric-related pitfalls in image analysis validation

Reinke, A., Tizabi, M. D., Baumgartner, M., Eisenmann, M., Heckmann-Nötzel, D., Kavur, A. E., Rädsch, T., Sudre, C. H., Acion, L., Antonelli, M., et al. (2024) · Nature Methods

Cell Image Analysis TechniquesMedical Image Segmentation Techniques

Cited by 241

Assessment of demographic, genetic, and imaging variables associated with brain resilience and cognitive resilience to pathological tau in patients with Alzheimer disease

Ossenkoppele, R., Lyoo, C. H., Jester-Broms, J., Sudre, C. H., Cho, H., Ryu, Y. H., Choi, J. Y., Smith, R., Strandberg, O., Palmqvist, S., et al. (2020) · JAMA Neurology

Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatments

Cited by 161

Common Limitations of Image Processing Metrics: A Picture Story

Reinke, A., Tizabi, M. D., Sudre, C. H., Eisenmann, M., Rädsch, T., Baumgartner, M., Acion, L., Antonelli, M., Arbel, T., Bakas, S., et al. (2021) · arXiv

Medical Image Segmentation TechniquesCell Image Analysis Techniques

Cited by 126

’Anxiety and depression symptoms after COVID-19 infection: results from the COVID Symptom Study app ’

Klaser, K., Thompson, E. J., Nguyen, L. H., Sudre, C. H., Antonelli, M., Murray, B., Canas, L. S., Molteni, E., Graham, M. S., Kerfoot, E., et al. (2021) · Journal of Neurology, Neurosurgery & Psychiatry

COVID-19 and Mental HealthLong-Term Effects of COVID-19

Cited by 84

Brain amyloid and vascular risk are related to distinct white matter hyperintensity patterns

Pålhaugen, L., Sudre, C. H., Tecelao, S., Nakling, A., Almdahl, I. S., Kalheim, L. F., Cardoso, M. J., Johnsen, S. H., Rongve, A., Aarsland, D., et al. (2021) · Journal of Cerebral Blood Flow & Metabolism

Cerebrovascular and genetic disordersDementia and Cognitive Impairment Research

Cited by 79

Patterns of white matter hyperintensities associated with cognition in middle-aged cognitively healthy individuals

Study, F. T. A., Brugulat-Serrat, A., Salvadó, G., Sudre, C. H., Grau-Rivera, O., Suárez-Calvet, M., Falcon, C., Sánchez-Benavides, G., Gramunt, N., Fauria, K., et al. (2020) · Brain Imaging and Behavior

Dementia and Cognitive Impairment ResearchCerebrovascular and genetic disorders

Cited by 64

Metrics reloaded: Recommendations for image analysis validation

Maier-Hein, L., Reinke, A., Godau, P., Tizabi, M. D., Buettner, F., Christodoulou, E., Glocker, B., Isensee, F., Kleesiek, J., Kozubek, M., et al. (2022) · arXiv

Cell Image Analysis TechniquesMedical Image Segmentation Techniques

Cited by 59

Amyloid pathology and vascular risk are associated with distinct patterns of cerebral white matter hyperintensities: A multicenter study in 3132 memory clinic patients

Biesbroek, J. M., Coenen, M., DeCarli, C., Fletcher, E. M., Maillard, P. M., Initiative, A. D. N., Barkhof, F., Barnes, J., Benke, T., Chen, C. P. L. H., et al. (2024) · Alzheimer's & Dementia

Cerebrovascular and genetic disordersDementia and Cognitive Impairment Research

Cited by 38

Regional associations of white matter hyperintensities and early cortical amyloid pathology

Lorenzini, L., Ansems, L. T., Lopes Alves, I., Ingala, S., Vállez García, D., Tomassen, J., Sudre, C., Salvadó, G., Shekari, M., Operto, G., et al. (2022) · Brain Communications

Dementia and Cognitive Impairment ResearchCerebrovascular and genetic disorders

Cited by 36

The relation between APOE genotype and cerebral microbleeds in cognitively unimpaired middle- and old-aged individuals

Ingala, S., Mazzai, L., Sudre, C. H., Salvadó, G., Brugulat-Serrat, A., Wottschel, V., Falcon, C., Operto, G., Tijms, B., Gispert, J. D., et al. (2020) · Neurobiology of Aging

Intracerebral and Subarachnoid Hemorrhage ResearchCerebrovascular and genetic disorders

Cited by 25

Geo-social gradients in predicted COVID-19 prevalence in Great Britain: results from 1 960 242 users of the COVID-19 Symptoms Study app

Bowyer, R. C. E., Varsavsky, T., Thompson, E. J., Sudre, C. H., Murray, B. A. K., Freidin, M. B., Yarand, D., Ganesh, S., Capdevila, J., Bakker, E., et al. (2021) · Thorax

COVID-19 epidemiological studiesData-Driven Disease Surveillance

Cited by 24

PIMMS: Permutation Invariant Multi-modal Segmentation

Varsavsky, T., Eaton-Rosen, Z., Sudre, C. H., Nachev, P., & Cardoso, M. J. (2018) · Lecture Notes in Computer Science

Medical Image Segmentation TechniquesBrain Tumor Detection and Classification

Cited by 22

Automatic Extraction of Hiatal Dimensions in 3-D Transperineal Pelvic Ultrasound Recordings

Williams, H., Cattani, L., Van Schoubroeck, D., Yaqub, M., Sudre, C., Vercauteren, T., D'Hooge, J., & Deprest, J. (2021) · Ultrasound in Medicine & Biology

Medical Image Segmentation TechniquesPelvic floor disorders treatments

Cited by 19