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Results: 48
Number of items: 48
  • Open Access
    Hampe, N. (2026). Deep learning-based derivation of physiological information from cardiac CT angiography. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    van Harten, L. D., de Jonge, C. S., Struik, F., Stoker, J., & Išgum, I. (2025). Quantitative Analysis of Small Intestinal Motility in 3D Cine‐MRI Using Centerline‐Aware Motion Estimation. Journal of Magnetic Resonance Imaging, 61(4), 1956-1966. https://doi.org/10.1002/jmri.29571
  • Open Access
    Karkalousos, D. (2025). Deep multitask learning for accelerating Magnetic Resonance Imaging. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    van Herten, R. L. M. (2025). Prior-informed deep learning for the analysis and fusion of cardiovascular imaging modalities. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Płotka, S., Szczepański, T., Szenejko, P., Korzeniowski, P., Rodriguez Calvo, J., Khalil, A., Shamshirsaz, A., Brawura-Biskupski-Samaha, R., Išgum, I., Sánchez, C. I., & Sitek, A. (2025). Real-time placental vessel segmentation in fetoscopic laser surgery for Twin-to-Twin Transfusion Syndrome. Medical Image Analysis, 99, Article 103330. https://doi.org/10.1016/j.media.2024.103330
  • Open Access
    Alvarez-Florez, L., Sander, J., Bourfiss, M., Tjong, F. V. Y., Velthuis, B. K., & Išgum, I. (2024). Deep Learning for Automatic Strain Quantification in Arrhythmogenic Right Ventricular Cardiomyopathy. In O. Camara, E. Puyol-Antón, M. Sermesant, A. Suinesiaputra, Q. Tao, C. Wang, & A. Young (Eds.), Statistical Atlases and Computational Models of the Heart: Regular and CMRxRecon Challenge Papers: 14th International Workshop, STACOM 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023 : revised selected papers (pp. 25–34). (Lecture Notes in Computer Science; Vol. 14507). Springer. https://doi.org/10.1007/978-3-031-52448-6_3
  • Open Access
    Kolk, M. Z. H., Ruipérez-Campillo, S., Allaart, C. P., Wilde, A. A. M., Knops, R. E., Narayan, S. M., Tjong, F. V. Y., & DEEP RISK investigators (2024). Multimodal explainable artificial intelligence identifies patients with non-ischaemic cardiomyopathy at risk of lethal ventricular arrhythmias. Scientific Reports, 14, Article 14889. https://doi.org/10.1038/s41598-024-65357-x
  • Open Access
    van Erck, D., Moeskops, P., Schoufour, J. D., Weijs, P. J. M., Scholte op Reimer, W. J. M., van Mourik, M. S., Planken, R. N., Vis, M. M., Baan, J., Išgum, I., Henriques, J. P., de Vos, B. D., & Delewi, R. (2024). Low muscle quality on a procedural computed tomography scan assessed with deep learning as a practical useful predictor of mortality in patients with severe aortic valve stenosis. Clinical Nutrition ESPEN, 63, 142–147. https://doi.org/10.1016/j.clnesp.2024.06.013
  • Open Access
    Koop, Y., Atsma, F., Batenburg, M. C. T., Meijer, H., van der Leij, F., Gal, R., van Velzen, S. G. M., Isgum, I., Vermeulen, H., Maas, A. H. E. M., Messaoudi, S. E., & Verkooijen, H. M. (2024). Competing risk analysis of cardiovascular disease risk in breast cancer patients receiving a radiation boost. Cardio-Oncology, 10, Article 7. https://doi.org/10.1186/s40959-024-00206-4
  • 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
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