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Quantifying segmentation uncertainty has become an important issue in medical image analysis due to the inherent ambiguity of anatomical structures and its pathologies.
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Erdil, E., Yildirim, S., Cetin, M., Tasdizen, T.: Mcmc shape sampling for image segmentation with nonparametric shape priors. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2016)
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Brügger, R., Baumgartner, C.F., Konukoglu, E.: A partially reversible U-Net for memory-efficient volumetric image segmentation. In: Medical Image Computing and Computer Assisted Intervention - MICCAI 2019 - 22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceedings, Part III. pp. 429–437 (2019). https://doi.org/10.1007/978-3-030-32248-9_48, https://doi.org/10.1007/978-3-030-32248-9_48
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Erdil, E., Yildirim, S., Tasdizen, T., Cetin, M.: Pseudo-marginal mcmc sampling for image segmentation using nonparametric shape priors. vol. 28, pp. 5702–5715 (Nov 2019). https://doi.org/10.1109/TIP.2019.2922071
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Kohl, S., Romera-Paredes, B., Meyer, C., Fauw, J.D., Ledsam, J.R., Maier-Hein, K.H., Eslami, S.M.A., Rezende, D.J., Ronneberger, O.: A probabilistic u-net for segmentation of ambiguous images. In: Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, 3-8 December 2018, Montréal, Canada. pp. 6965–6975 (2018), http://papers.nips.cc/paper/7928-a-probabilistic-u-net-for-segmentation-of-ambiguous-images
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2019
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