Using a teacher network's soft labels on unlabeled target images to train a student network yields improved white matter hyperintensity segmentation over baseline and adversarial adaptation in most but not all cross-scanner scenarios.
In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
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Knowledge distillation for semi-supervised domain adaptation
Using a teacher network's soft labels on unlabeled target images to train a student network yields improved white matter hyperintensity segmentation over baseline and adversarial adaptation in most but not all cross-scanner scenarios.