Cardoso, M. Jorge

The Future of Digital Health with Federated Learning

Rieke, N., Hancox, J., Li, W., Milletari, F., Roth, H., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B., Maier-Hein, K., et al. (2020) · arXiv

Privacy-Preserving Technologies in DataArtificial Intelligence in Healthcare and Education

Cited by 3,495

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

The Medical Segmentation Decathlon

Antonelli, M., Reinke, A., Bakas, S., Farahani, K., Kopp-Schneider, A., Landman, B. A., Litjens, G., Menze, B., Ronneberger, O., Summers, R. M., et al. (2022) · Nature Communications

Medical Image Segmentation TechniquesAdvanced Neural Network Applications

Cited by 1,384

FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Lekadir, K., Frangi, A. F., Porras, A. R., Glocker, B., Cintas, C., Langlotz, C. P., Weicken, E., Asselbergs, F. W., Prior, F., Collins, G. S., et al. (2025) · BMJ

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 957

Privacy-Preserving Federated Brain Tumour Segmentation

Li, W., Milletarì, F., Xu, D., Rieke, N., Hancox, J., Zhu, W., Baust, M., Cheng, Y., Ourselin, S., Cardoso, M. J., et al. (2019) · Lecture Notes in Computer Science

Privacy-Preserving Technologies in DataBrain Tumor Detection and Classification

Cited by 578

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

Presymptomatic cognitive and neuroanatomical changes in genetic frontotemporal dementia in the Genetic Frontotemporal dementia Initiative (GENFI) study: a cross-sectional analysis

Rohrer, J. D., Nicholas, J. M., Cash, D. M., van Swieten, J., Dopper, E., Jiskoot, L., van Minkelen, R., Rombouts, S. A., Cardoso, M. J., Clegg, S., et al. (2015) · The Lancet Neurology

Dementia and Cognitive Impairment ResearchAmyotrophic Lateral Sclerosis Research

Cited by 538

Brain Imaging Generation with Latent Diffusion Models

Pinaya, W. H. L., Tudosiu, P.-D., Dafflon, J., Da Costa, P. F., Fernandez, V., Nachev, P., Ourselin, S., & Cardoso, M. J. (2022) · Lecture Notes in Computer Science

Generative Adversarial Networks and Image SynthesisArtificial Intelligence in Healthcare and Education

Cited by 395

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

Accurate multimodal probabilistic prediction of conversion to Alzheimer’s disease in patients with mild cognitive impairment

Young, J., Modat, M., Cardoso, M. J., Mendelson, A., Cash, D., & Ourselin, S. (2013) · NeuroImage Clinical

Dementia and Cognitive Impairment ResearchArtificial Intelligence in Healthcare

Cited by 249

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

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

In vivo imaging of tau pathology using multi-parametric quantitative MRI

Wells, J. A., O'Callaghan, J. M., Holmes, H. E., Powell, N. M., Johnson, R. A., Siow, B., Torrealdea, F., Ismail, O., Walker-Samuel, S., Golay, X., et al. (2015) · NeuroImage

Alzheimer's disease research and treatmentsDementia and Cognitive Impairment Research

Cited by 93

Longitudinal measurement of the developing grey matter in preterm subjects using multi-modal MRI

Eaton-Rosen, Z., Melbourne, A., Orasanu, E., Cardoso, M. J., Modat, M., Bainbridge, A., Kendall, G. S., Robertson, N. J., Marlow, N., & Ourselin, S. (2015) · NeuroImage

Advanced Neuroimaging Techniques and ApplicationsNeonatal and fetal brain pathology

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

Assessing atrophy measurement techniques in dementia: results from the MIRIAD atrophy challenge

Cash, D. M., Frost, C., Iheme, L. O., Ünay, D., Kandemir, M., Fripp, J., Salvado, O., Bourgeat, P., Reuter, M., Fischl, B., et al. (2015) · NeuroImage

Dementia and Cognitive Impairment ResearchMedical Image Segmentation Techniques

Cited by 72

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

Comparison of in vivo and ex vivo MRI for the detection of structural abnormalities in a mouse model of tauopathy

Holmes, H. E., Powell, N. M., Ma, D., Ismail, O., Harrison, I. F., Wells, J. A., Colgan, N., O'Callaghan, J. M., Johnson, R. A., Murray, T. K., et al. (2017) · Frontiers in Neuroinformatics

Alzheimer's disease research and treatmentsDementia and Cognitive Impairment Research

Cited by 55

Imaging the accumulation and suppression of tau pathology using multiparametric MRI

Holmes, H. E., Colgan, N., Ismail, O., Ma, D., Powell, N. M., O'Callaghan, J. M., Harrison, I. F., Johnson, R. A., Murray, T. K., Ahmed, Z., et al. (2016) · Neurobiology of Aging

Alzheimer's disease research and treatmentsDementia and Cognitive Impairment Research

Cited by 51

Study the Longitudinal in vivo and Cross-Sectional ex vivo Brain Volume Difference for Disease Progression and Treatment Effect on Mouse Model of Tauopathy Using Automated MRI Structural Parcellation

Ma, D., Holmes, H. E., Cardoso, M. J., Modat, M., Harrison, I. F., Powell, N. M., O’Callaghan, J. M., Ismail, O., Johnson, R. A., O’Neill, M. J., et al. (2019) · Frontiers in Neuroscience

Dementia and Cognitive Impairment ResearchMedical Image Segmentation Techniques

Cited by 36

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

MRI to X-ray mammography registration using a volume-preserving affine transformation

Mertzanidou, T., Hipwell, J., Cardoso, M. J., Zhang, X., Tanner, C., Ourselin, S., Bick, U., Huisman, H., Karssemeijer, N., & Hawkes, D. (2012) · Medical Image Analysis

Medical Image Segmentation TechniquesDigital Radiography and Breast Imaging

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