A cycle-free target-guided GAN that transfers source images into target style, combined with self-ensembling, achieves state-of-the-art synthetic-to-real semantic segmentation adaptation on GTA5-to-Cityscapes and SYNTHIA-to-Cityscapes.
Unsupervised domain adaptation for medical imaging segmentation with self-ensembling
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abstract
Recent advances in deep learning methods have come to define the state-of-the-art for many medical imaging applications, surpassing even human judgment in several tasks. Those models, however, when trained to reduce the empirical risk on a single domain, fail to generalize when applied to other domains, a very common scenario in medical imaging due to the variability of images and anatomical structures, even across the same imaging modality. In this work, we extend the method of unsupervised domain adaptation using self-ensembling for the semantic segmentation task and explore multiple facets of the method on a small and realistic publicly-available magnetic resonance (MRI) dataset. Through an extensive evaluation, we show that self-ensembling can indeed improve the generalization of the models even when using a small amount of unlabelled data.
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Self-Ensembling with GAN-based Data Augmentation for Domain Adaptation in Semantic Segmentation
A cycle-free target-guided GAN that transfers source images into target style, combined with self-ensembling, achieves state-of-the-art synthetic-to-real semantic segmentation adaptation on GTA5-to-Cityscapes and SYNTHIA-to-Cityscapes.