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A Surprisingly Effective Perimeter-based Loss for Medical Image Segmentation.

, , , , and . MIDL, volume 143 of Proceedings of Machine Learning Research, page 158-167. PMLR, (2021)

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Pattern Spotting in Historical Documents Using Convolutional Models., , , , and . HIP@ICDAR, page 60-65. ACM, (2019)A Surprisingly Effective Perimeter-based Loss for Medical Image Segmentation., , , , and . MIDL, volume 143 of Proceedings of Machine Learning Research, page 158-167. PMLR, (2021)SegTHOR: Segmentation of Thoracic Organs at Risk in CT images., , , and . CoRR, (2019)Classification of In-Vivo Endomicroscopic Images of the Alveolar Respiratory System., , , , and . MVA, page 471-474. (2009)On the Inclusion of Topological Requirements in CNNs for Semantic Segmentation Applied to Radiotherapy., , and . SSVM, volume 14009 of Lecture Notes in Computer Science, page 363-375. Springer, (2023)High-level Prior-based Loss Functions for Medical Image Segmentation: A Survey., , , , and . CoRR, (2020)Can SegFormer be a True Competitor to U-Net for Medical Image Segmentation?, , , and . MIUA, volume 14122 of Lecture Notes in Computer Science, page 111-118. Springer, (2023)Pruning Trees in Random Forests for Minimizing non Detection in Medical imaging., , and . Handbook of Pattern Recognition and Computer Vision, World Scientific, (2016)Automatic cardiac ventricle segmentation in MR images: a validation study., , , , and . Int. J. Comput. Assist. Radiol. Surg., 6 (5): 573-581 (2011)Investigating CoordConv for Fully and Weakly Supervised Medical Image Segmentation., , , , and . IPTA, page 1-5. IEEE, (2020)