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Deep convolutional neural networks (CNN) have proven to be remarkably effective in semantic segmentation tasks.
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Caicedo, J.C., Goodman, A., Karhohs, K.W., Cimini, B.A., Ackerman, J., Haghighi, M., Heng, C., Becker, T., Doan, M., McQuin, C., et al.: Nucleus segmentation across imaging experiments: the 2018 data science bowl. Nature methods 16
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Elliott, C., Wolinsky, J.S., Hauser, S.L., Kappos, L., Barkhof, F., Bernasconi, C., Wei, W., Belachew, S., Arnold, D.L.: Slowly expanding/evolving lesions as a magnetic resonance imaging marker of chronic active multiple sclerosis lesions. Multiple Sclerosis Journal 25
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Falk, T., Mai, D., Bensch, R., Çiçek, Ö., Abdulkadir, A., Marrakchi, Y., Böhm, A., Deubner, J., Jäckel, Z., Seiwald, K., et al.: U-net: deep learning for cell counting, detection, and morphometry. Nature methods 16
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Zhu, W., Huang, Y., Zeng, L., Chen, X., Liu, Y., Qian, Z., Du, N., Fan, W., Xie, X.: Anatomynet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics 46
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Eelbode, T., Bertels, J., Berman, M., Vandermeulen, D., Maes, F., Bisschops, R., Blaschko, M.B.: Optimization for medical image segmentation: theory and practice when evaluating with dice score or jaccard index. IEEE Transactions on Medical Imaging 39
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Shirokikh, B., Shevtsov, A., Kurmukov, A., Dalechina, A., Krivov, E., Kostjuchenko, V., Golanov, A., Belyaev, M.: Universal loss reweighting to balance lesion size inequality in 3d medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 523–532, Springer (2020)
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Kofler, F., Ezhov, I., Isensee, F., Balsiger, F., Berger, C., Koerner, M., Paetzold, J., Li, H., Shit, S., McKinley, R., Bakas, S., Zimmer, C., Ankerst, D., Kirschke, J., Wiestler, B., Menze, B.H.: Are we using appropriate segmentation metrics? identifying correlates of human expert perception for cnn training beyond rolling the dice coefficient (2021)
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Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022)
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