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Results: 48
Number of items: 48
  • Open Access
    Hampe, N., van Velzen, S. G. M., Wolterink, J. M., Collet, C., Henriques, J. P. S., Planken, N., & Išgum, I. (2024). Graph neural networks for automatic extraction and labeling of the coronary artery tree in CT angiography. Journal of Medical Imaging, 11(03), Article 034001 . https://doi.org/10.1117/1.jmi.11.3.034001
  • Open Access
    Williams, M. C., Weir-McCall, J. R., Baldassarre, L. A., De Cecco, C. N., Choi, A. D., Dey, D., Dweck, M. R., Isgum, I., Kolossvary, M., Leipsic, J., Lin, A., Lu, M. T., Motwani, M., Nieman, K., Shaw, L., van Assen, M., & Nicol, E. (2024). Artificial Intelligence and Machine Learning for Cardiovascular Computed Tomography (CCT): A White Paper of the Society of Cardiovascular Computed Tomography (SCCT). Journal of cardiovascular computed tomography, 18(6), 519–532. https://doi.org/10.1016/j.jcct.2024.08.003
  • Open Access
    van de Vijver, W. R., Hennecken, J., Lagogiannis, I., Pérez del Villar, C., Herrera, C., Douek, P. C., Segev, A., Hovingh, G. K., Išgum, I., Winter, M. M., Planken, R. N., & Claessen, B. E. P. M. (2024). The Role of Coronary Computed Tomography Angiography in the Diagnosis, Risk Stratification, and Management of Patients with Diabetes and Chest Pain. Reviews in Cardiovascular Medicine, 25(12), Article 442. https://doi.org/10.31083/j.rcm2512442
  • Open Access
    Karkalousos, D., Išgum, I., Marquering, H. A., & Caan, M. W. A. (2024). Atommic: An Advanced Toolbox for Multitask Medical Imaging Consistency to Facilitate Artificial Intelligence Applications from Acquisition to Analysis in Magnetic Resonance Imaging. Computer Methods and Programs in Biomedicine, 256, Article 108377. https://doi.org/10.1016/j.cmpb.2024.108377
  • Open Access
    van Herten, R. L. M., Lagogiannis, I., Leiner, T., & Išgum, I. (2024). The role of artificial intelligence in coronary CT angiography. Netherlands Heart Journal, 32(11), 417–425. https://doi.org/10.1007/s12471-024-01901-8
  • Open Access
    Föllmer, B., Williams, M. C., Dey, D., Arbab-Zadeh, A., Maurovich-Horvat, P., Volleberg, R. H. J. A., Rueckert, D., Schnabel, J. A., Newby, D. E., Dweck, M. R., Guagliumi, G., Falk, V., Vázquez-Mézquita, A. J., Biavati, F., Išgum, I., & Dewey, M. (2024). Roadmap on the Use of Artificial Intelligence for Imaging of Vulnerable Atherosclerotic Plaque in Coronary Arteries. In I. Sack, & T. Schaeffter (Eds.), Quantification of Biophysical Parameters in Medical Imaging (2nd ed., pp. 547–568). Springer. https://doi.org/10.1007/978-3-031-61846-8_27
  • Open Access
    Płotka, S. S. (2024). Enhancing prenatal care through deep learning. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    van Harten, L. D. (2024). Motion analysis in 4D MRI of the small intestine using neural networks. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Oudkerk Pool, M. D. (2024). Innovations in cardiology: Towards patient centered care. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Föllmer, B., Williams, M. C., Dey, D., Arbab-Zadeh, A., Maurovich-Horvat, P., Volleberg, R. H. J. A., Rueckert, D., Schnabel, J. A., Newby, D. E., Dweck, M. R., Guagliumi, G., Falk, V., Vázquez-Mézquita, A. J., Biavati, F., Išgum, I., & Dewey, M. (2024). Roadmap on the Use of Artificial Intelligence for Imaging of Vulnerable Atherosclerotic Plaque in Coronary Arteries. Nature Reviews. Cardiology, 21(1), 51-64. https://doi.org/10.1038/s41569-023-00900-3
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