Cross-Modality Domain Adaptation Challenge 2022 (crossMoDA)

<p>Official training and validation sets of crossMoDA 2022.</p> <p><strong>All data will be made available online with a permissive non-commercial copyright-license (<a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a>), allowing for data to be shared, distributed and improved upon</strong>.</p> <p>&nbsp;</p> <p>If you use the data, please cite:</p> <p>1. Shapey, J., Kujawa, A., Dorent, R., Wang, G., Bisdas, S., Dimitriadis, A., Grishchuck, D., Paddick, I., Kitchen, N., Bradford, R., Saeed, S., Ourselin, S., &amp; Vercauteren, T. (2021). Segmentation of Vestibular Schwannoma from Magnetic Resonance Imaging: An Open Annotated Dataset and Baseline Algorithm [Data set]. The Cancer Imaging Archive. <a href="https://doi.org/10.7937/TCIA.9YTJ-5Q73">https://doi.org/10.7937/TCIA.9YTJ-5Q73</a>&nbsp;</p> <p>2. Dorent, R. et al (2022).&nbsp; CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation.&nbsp; ArXiv <a href="https://arxiv.org/abs/2201.02831">https://arxiv.org/abs/2201.02831</a></p> <p>&nbsp;</p> <p>Acknowledgments:</p> <p>This challenge is supported by Wellcome Trust (203145Z/16/Z, 203148/Z/16/Z), EPSRC (NS/A000050/1,<br> NS/A000049/1) and ZonMw (project number: 10070012010006) funding. All the organizers will have access to the<br> test set if needed.</p>