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Results: 42
Number of items: 42
  • 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
  • Open Access
    Jansen, G. E., de Vos, B. D., Molenaar, M. A., Schuuring, M. J., Bouma, B. J., & Išgum, I. (2024). Automated echocardiography view classification and quality assessment with recognition of unknown views. Journal of Medical Imaging, 11(5), Article 054002. https://doi.org/10.1117/1.jmi.11.5.054002
  • Open Access
    Płotka, S. S. (2024). Enhancing prenatal care through deep learning. [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
    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
    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
    Zoetmulder, R. (2023). Deep-learning-based image segmentation for uncommon ischemic stroke: From infants to adults. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Sander, J. (2023). Assessing anatomy and function of the heart using 4D cardiac MRI and deep learning. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Ties, D., van Dorp, P., Pundziute, G., van der Aalst, C. M., Gratama, J. W. C., Braam, R. L., Kuijpers, D., Lubbers, D. D., van der Bilt, I. A. C., Westenbrink, B. D., Oude Wolcherink, M. J., Doggen, C. J. M., Išgum, I., Nijveldt, R., de Koning, H. J., Vliegenthart, R., Oudkerk, M., & van der Harst, P. (2022). Early detection of obstructive coronary artery disease in the asymptomatic high-risk population: objectives and study design of the EARLY-SYNERGY trial. American Heart Journal, 246, 166-177. https://doi.org/10.1016/j.ahj.2022.01.005
  • Schreuder, A., Jacobs, C., Lessmann, N., Broeders, M. J. M., Silva, M., Išgum, I., de Jong, P. A., van den Heuvel, M. M., Sverzellati, N., Prokop, M., Pastorino, U., Schaefer-Prokop, C. M., & van Ginneken, B. (2022). Scan-based competing death risk model for re-evaluating lung cancer computed tomography screening eligibility. The European Respiratory Journal, 59(5), Article 2101613. https://doi.org/10.1183/13993003.01613-2021
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