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Convolutional neural networks trained on publicly available medical imaging datasets (source domain) rarely generalise to different scanners or acquisition protocols (target domain).
Styner, M., Lee, J., Chin, B., Chin, M., Commowick, O., Tran, H.: 3d segmentation in the clinic: A grand challenge ii: Ms lesion segmentation (2008)
2008
Earlier work this paper cites.
2010
Earlier work this paper cites.
Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (brats). IEEE transactions on medical imaging 34
2014
Earlier work this paper cites.
Ganin, Y., et al.: Domain-adversarial training of neural networks. The Journal of Machine Learning Research 17
2016
Earlier work this paper cites.
Carass, A., et al.: Longitudinal multiple sclerosis lesion segmentation: Resource and challenge. NeuroImage 148
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Kamnitsas, K., et al.: Unsupervised domain adaptation in brain lesion segmentation with adversarial networks. In: International conference on information processing in medical imaging. pp. 597–609. Springer (2017)
2017
Cited alongside, same era.
Commowick, O., et al.: Objective evaluation of multiple sclerosis lesion segmentation using a data management and processing infrastructure. Scientific reports 8
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Carlucci, F.M., et al.: Domain generalization by solving jigsaw puzzles. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2229–2238 (2019)
2019
Cited alongside, same era.
Ma, C., Ji, Z., Gao, M.: Neural style transfer improves 3d cardiovascular mr image segmentation on inconsistent data. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 128–136. Springer (2019)
2019
Later among the works it cites.
Orbes-Arteaga, M., Varsavsky, T., et al.: Multi-domain adaptation in brain mri through paired consistency and adversarial learning. In: Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data. pp. 54–62. Springer International Publishing, Cham (2019)
2019
Later among the works it cites.
Perone, C.S., et al.: Unsupervised domain adaptation for medical imaging segmentation with self-ensembling. NeuroImage (2019)
2019
Later among the works it cites.
Shaw, R., et al.: MRI k-space motion artefact augmentation: Model robustness and task-specific uncertainty. In: MIDL. pp. 427–436 (2019)
2019
Later among the works it cites.
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Isensee, F., et al.: Automated brain extraction of multisequence mri using artificial neural networks. Human brain mapping 40
2019
Cited alongside, same era.
Kuijf, H.J., et al.: Standardized assessment of automatic segmentation of white matter hyperintensities; results of the wmh segmentation challenge. IEEE transactions on medical imaging (2019)
2019
Cited alongside, same era.
2019
Later among the works it cites.
Valverde, S., et al.: One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks. NeuroImage: Clinical 21
2019
Later among the works it cites.