Augmentation Via Registration: AutoImplant 2020 Augmented Data Set
Description
Augmented data derived from the AutoImplant 2020 Challenge. Data has been augmented via non-linear SyN registrations using Advanced Normalization Tools (ANTs). This data was used to obtain first place in the AutoImplant 2020 challenge. The AutoImplant 2020 Challenge data was derived from the QC500 dataset from qure.ai. The original dataset is licensed under the CC BY-NC-SA 4.0 license (Attribution-NonCommercial-ShareAlike) and complies with the End User License Agreement (EULA) which are both detailed in the "LICENSE" file in this folder. Please refer to the "LICENSE" file for terms of use.
If you use this data, please cite the following paper:
Ellis D.G., Aizenberg M.R. (2020) Deep Learning Using Augmentation via Registration: 1st Place Solution to the AutoImplant 2020 Challenge. In: Li J., Egger J. (eds) Towards the Automatization of Cranial Implant Design in Cranioplasty. AutoImplant 2020. Lecture Notes in Computer Science, vol 12439. Springer, Cham. https://doi.org/10.1007/978-3-030-64327-0_6
Files
Files
(10.6 GB)
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md5:8374aebc4ff866d15dab1e3f2d49ef0f
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10.6 GB | Download |
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md5:1dfa11e24964df6e120ec2522ffd6272
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11.9 kB | Download |
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md5:2188c8b16b054faea66e7934cba5630a
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1.0 kB | Download |
Additional details
References
- Ellis D.G., Aizenberg M.R. (2020) Deep Learning Using Augmentation via Registration: 1st Place Solution to the AutoImplant 2020 Challenge. In: Li J., Egger J. (eds) Towards the Automatization of Cranial Implant Design in Cranioplasty. AutoImplant 2020. Lecture Notes in Computer Science, vol 12439. Springer, Cham. https://doi.org/10.1007/978-3-030-64327-0_6