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 ## [Applications](https://github.com/andreped/GradientAccumulator#applications)
+* Støverud et al., AeroPath: An airway segmentation benchmark dataset with challenging pathology (2023), arXiv (preprint), https://doi.org/10.48550/arXiv.2311.01138
 * Bouget et al., Raidionics: an open software for pre- and postoperative central nervous system tumor segmentation and standardized reporting (2023), Scientific Reports, https://doi.org/10.1038/s41598-023-42048-7
-* Helland et al., Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks (2023), arXiv (preprint), https://arxiv.org/abs/2304.08881
+* Helland et al., Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks (2023), Scientific Reports, https://doi.org/10.1038/s41598-023-45456-x
 * Pérez de Frutos et al., Learning deep abdominal CT registration through adaptive loss weighting and synthetic data generation (2023), PLOS ONE, https://doi.org/10.1371/journal.pone.0282110
 * Bouget et al., Preoperative Brain Tumor Imaging: Models and Software for Segmentation and Standardized Reporting, (2022) Frontiers in Neurology, https://doi.org/10.3389/fneur.2022.932219
 * Pedersen et al., H2G-Net: A multi-resolution refinement approach for segmentation of breast cancer region in gigapixel histopathological images (2022), Frontiers in Medicine, https://doi.org/10.3389/fmed.2022.971873