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Results: 42
Number of items: 42
  • Open Access
    Hampe, N. (2026). Deep learning-based derivation of physiological information from cardiac CT angiography. [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
    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
    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
    Karkalousos, D. (2025). Deep multitask learning for accelerating Magnetic Resonance Imaging. [Thesis, fully internal, Universiteit van Amsterdam].
  • 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
    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
    Galanty, M., Luitse, D., Noteboom, S. H., Croon, P., Vlaar, A. P., Poell, T., Sánchez Gutiérrez, C. I., Blanke, T., & Išgum, I. (2024). Assessing the documentation of publicly available medical image and signal datasets and their impact on bias using the BEAMRAD tool. Scientific Reports, 14, Article 31846. https://doi.org/10.1038/s41598-024-83218-5
  • 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
  • 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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