Items where Author is "Glocker, B."
Article
Lekadir, K. 
ORCID: 0000-0002-9456-1612, Frangi, A. F., Porras, A. R. , Glocker, B., Cintas, C., Langlotz, C. P., Weicken, E., Asselbergs, F. W., Prior, F., Collins, G. S., Kaissis, G., Tsakou, G., Buvat, I., Kalpathy-Cramer, J., Mongan, J., Schnabel, J. A., Kushibar, K., Riklund, K., Marias, K., Amugongo, L. M., Fromont, L. A., Maier-Hein, L., Cerdá-Alberich, L., Martí-Bonmatí, L., Cardoso, M. J., Bobowicz, M., Shabani, M., Tsiknakis, M., Zuluaga, M. A., Fritzsche, M-C., Camacho, M., Linguraru, M. G., Wenzel, M., De Bruijne, M., Tolsgaard, M. G., Goisauf, M., Cano Abadía, M., Papanikolaou, N., Lazrak, N., Pujol, O., Osuala, R., Napel, S., Colantonio, S., Joshi, S., Klein, S., Aussó, S., Rogers, W. A., Salahuddin, Z., Starmans, M. P. A., FUTURE-AI Consortium & Botwe, B. O. ORCID: 0000-0002-0477-640X (2025).
FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare.
BMJ, 388,
article number e081554.
doi: 10.1136/bmj-2024-081554
Tarroni, G. ORCID: 0000-0002-0341-6138, Bai, W., Oktay, O. , Schuh, A., Suzuki, H., Glocker, B., Matthews, P. M. & Rueckert, D. (2020).
Large-scale Quality Control of Cardiac Imaging in Population Studies: Application to UK Biobank.
Scientific Reports, 10(1),
article number 2408.
doi: 10.1038/s41598-020-58212-2
Tarroni, G. ORCID: 0000-0002-0341-6138, Oktay, O., Bai, W. , Schuh, A., Suzuki, H., Passerat-Palmbach, J., De Marvao, A., O’Regan, D. P., Cook, S., Glocker, B., Matthews, P M. & Rueckert, D. (2019).
Learning-based quality control for cardiac MR images.
IEEE Transactions on Medical Imaging, 38(5),
pp. 1127-1138.
doi: 10.1109/tmi.2018.2878509
Bai, W., Sinclair, M., Tarroni, G. ORCID: 0000-0002-0341-6138 , Oktay, O., Rajchl, M., Vaillant, G., Lee, A. M., Aung, N., Lukaschuk, E., Sanghvi, M. M., Zemrak, F., Fung, K., Paiva, J. M., Carapella, V., Kim, Y. J., Suzuki, H., Kainz, B., Matthews, P M., Petersen, S. E., Piechnik, S. K., Neubauer, S., Glocker, B. & Rueckert, D. (2018).
Automated cardiovascular magnetic resonance image analysis with fully convolutional networks.
Journal of Cardiovascular Magnetic Resonanc, 20(1),
article number 65.
doi: 10.1186/s12968-018-0471-x
Book Section
Al-Arif, S. M., Gundry, M., Knapp, K. & Slabaugh, G. G. (2017). Improving an Active Shape Model with Random Classification Forest for Segmentation of Cervical Vertebrae. In: Yao, J., Vrtovec, T., Zheng, G. , Frangi, A., Glocker, B. & Li, S. (Eds.), Improving an Active Shape Model with Random Classification Forest for Segmentation of Cervical Vertebrae. Lecture Notes in Computer Science, 10182. (pp. 3-15). Cham: Springer.
Conference or Workshop Item
Wang, S., Tarroni, G. ORCID: 0000-0002-0341-6138, Qin, C. , Mo, Y., Dai, C., Chen, C., Glocker, B., Guo, Y., Rueckert, D. & Bai, W. (2020).
Deep Generative Model-based Quality Control for Cardiac MRI Segmentation.
In:
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020.
23rd International Conference on Medical Image Computing and Computer Assisted Intervention, 04 - 08 October 2020, Lima, Peru.
doi: 10.1007/978-3-030-59719-1_9