Unsupervised domain adaptation in brain lesion segmentation with adversarial networks

Understand

Significant advances have been made towards building accurate automatic segmentation systems for a variety of biomedical applications using machine learning.

• However, the performance of these systems often degrades when they are applied on new data that differ from the training data, for example, due to variations in imaging protocols.
• Manually annotating new data for each test domain is not a feasible solution.
• In this work we investigate unsupervised domain adaptation using adversarial neural networks to train a segmentation method which is more invariant to differences in the input data, and which does not require any annotations on the test domain.