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  • Source: Scopus
20182021

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  • Improving Aleatoric Uncertainty Quantification in Multi-annotated Medical Image Segmentation with Normalizing Flows

    Valiuddin, M. M. A., Viviers, C. G. A., van Sloun, R. J. G., de With, P. H. N. & van der Sommen, F., 2021, Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis: 3rd International Workshop, UNSURE 2021, and 6th International Workshop, PIPPI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings. Sudre, C. H., Licandro, R., Baumgartner, C., Melbourne, A., Dalca, A., Hutter, J., Tanno, R., Abaci Turk, E., Van Leemput, K., Torrents Barrena, J., Wells, W. M. & Macgowan, C. (eds.). Cham: Springer, p. 75-88 14 p. (Lecture Notes in Computer Science (LNCS); vol. 12959)(Image Processing, Computer Vision, Pattern Recognition, and Graphics (LNIP); vol. 12959).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review