Search results

  • Full text

  • Document type

    • Publication year

  • Organisation

Results: 45
Number of items: 45
  • Open Access
    Zoetmulder, R. (2023). Deep-learning-based image segmentation for uncommon ischemic stroke: From infants to adults. [Thesis, fully internal, Universiteit van Amsterdam].
  • 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
  • van Velzen, S. G. M., Gal, R., Teske, A. J., van der Leij, F., van den Bongard, D. H. J. G., Viergever, M. A., Verkooijen, H. M., & Išgum, I. (2022). AI-Based Radiation Dose Quantification for Estimation of Heart Disease Risk in Breast Cancer Survivors After Radiation Therapy. International Journal of Radiation Oncology Biology Physics, 112(3), 621-632. https://doi.org/10.1016/j.ijrobp.2021.09.008
  • van Velzen, S. G. M., Bruns, S., Wolterink, J. M., Leiner, T., Viergever, M. A., Verkooijen, H. M., & Išgum, I. (2022). AI-Based Quantification of Planned Radiation Therapy Dose to Cardiac Structures and Coronary Arteries in Patients With Breast Cancer. International Journal of Radiation Oncology Biology Physics, 112(3), 611-620. https://doi.org/10.1016/j.ijrobp.2021.09.009
  • 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
  • Open Access
    Zoetmulder, R., Išgum, I., Gavves, E., & MR CLEAN Registry Investigators (2022). Deep-Learning-Based Thrombus Localization and Segmentation in Patients with Posterior Circulation Stroke. Diagnostics, 12(6), Article 1400. https://doi.org/10.3390/diagnostics12061400
  • Open Access
    Bruns, S., Wolterink, J. M., van den Boogert, T. P. W., Runge, J. H., Bouma, B. J., Henriques, J. P., Baan, J., Viergever, M. A., Planken, R. N., & Išgum, I. (2022). Deep learning-based whole-heart segmentation in 4D contrast-enhanced cardiac CT. Computers in Biology and Medicine, 142, Article 105191. https://doi.org/10.1016/j.compbiomed.2021.105191
  • Open Access
    Bruns, S. (2022). Automated segmentation of the heart in high-dimensional computed tomography. [Thesis, fully internal, Universiteit van Amsterdam].
  • de Vos, B. D., Lessmann, N., de Jong, P. A., & Išgum, I. (2021). Deep Learning-Quantified Calcium Scores for Automatic Cardiovascular Mortality Prediction at Lung Screening Low-Dose CT. Radiology. Cardiothoracic imaging, 3(2), Article e190219. https://doi.org/10.1148/ryct.2021190219
  • Wolterink, J. M., Mukhopadhyay, A., Leiner, T., Vogl, T. J., Bucher, A. M., & Išgum, I. (2021). Generative Adversarial Networks: A Primer for Radiologists. RadioGraphics, 41(3), 840-857. https://doi.org/10.1148/rg.2021200151
Page 3 of 5