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Adapting the Mean Teacher for keypoint-based lung registration under geometric domain shifts
[article]
2022
arXiv
pre-print
Recent deep learning-based methods for medical image registration achieve results that are competitive with conventional optimization algorithms at reduced run times. However, deep neural networks generally require plenty of labeled training data and are vulnerable to domain shifts between training and test data. While typical intensity shifts can be mitigated by keypoint-based registration, these methods still suffer from geometric domain shifts, for instance, due to different fields of view.
arXiv:2207.00371v1
fatcat:fq74dje5qzbi7cqs3r6rf4at4a