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Results: 25
Number of items: 25
  • Open Access
    Magg, C., ter Wee, M. A., Buijs, G. S., Kievit, A. J., Krap, D. A., Dobbe, J. G. G., Streekstra, G. J., Blankevoort, L., & Sánchez, C. I. (2024). Towards automation in non-invasive measurement of knee implant displacement. In W. Chen, & S. M. Astley (Eds.), Medical Imaging 2024: Computer-Aided Diagnosis: 19–22 February 2024, San Diego, California, United States Article 129270R (Proceedings of SPIE; Vol. 12927), (Progress in Biomedical Optics and Imaging; Vol. 25, No. 51). SPIE. https://doi.org/10.1117/12.3008090
  • Open Access
    de Vente, C., van Ginneken, B., Hoyng, C. B., Klaver, C. C. W., & Sánchez, C. I. (2024). Uncertainty-aware multiple-instance learning for reliable classification: Application to optical coherence tomography. Medical Image Analysis, 97, Article 103259. https://doi.org/10.1016/j.media.2024.103259
  • 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
    Maier-Hein, L., Reinke, A., Godau, P., Sánchez, C. I., & Metrics Reloaded (2024). Metrics reloaded: recommendations for image analysis validation. Nature Methods, 21(2), 195–212. https://doi.org/10.1038/s41592-023-02151-z
  • Open Access
    Yiasemis, G., Sánchez, C. I., Sonke, J.-J., & Teuwen, J. (2024). On retrospective k-space subsampling schemes for deep MRI reconstruction. Magnetic resonance imaging, 107, 33–46. https://doi.org/10.1016/j.mri.2023.12.012
  • Open Access
    Reinke, A., Tizabi, M. D., Baumgartner, M., Sánchez, C. I., & Metrics Reloaded (2024). Understanding metric-related pitfalls in image analysis validation. Nature Methods, 21(2), 182–194. https://doi.org/10.1038/s41592-023-02150-0
  • Open Access
    Sogancioglu, E., van Ginneken, B., Behrendt, F., Bengs, M., Schlaefer, A., Radu, M., Xu, D., Sheng, K., Scalzo, F., Marcus, E., Papa, S., Teuwen, J., Scholten, E. T., Schalekamp, S., Hendrix, N., Jacobs, C., Hendrix, W., Sánchez, C., & Murphy, K. (2024). Nodule Detection and Generation on Chest X-Rays: NODE21 Challenge. IEEE Transactions on Medical Imaging, 43(8), 2839–2853. https://doi.org/10.1109/tmi.2024.3382042
  • Open Access
    de Vente, C., Valmaggia, P., Hoyng, C. B., Holz, F. G., Islam, M. M., Klaver, C. C. W., Boon, C. J. F., Schmitz-Valckenberg, S., Tufail, A., Saßmannshausen, M., & Sánchez, C. I. (2024). Generalizable Deep Learning for the Detection of Incomplete and Complete Retinal Pigment Epithelium and Outer Retinal Atrophy: A MACUSTAR Report. Translational Vision Science and Technology, 13(9), Article 11. https://doi.org/10.1167/tvst.13.9.11
  • Open Access
    Płotka, S. S. (2024). Enhancing prenatal care through deep learning. [Thesis, fully internal, Universiteit van Amsterdam].
  • de Vente, C., & Sá‎nchez, C. I. (2022). Rotterdam EyePACS AIROGS Lite development and test set [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7178671
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