Data Augmentation-Based Unsupervised Domain Adaptation In Medical
  Imaging
Sebastian Nørgaard Llambias Mads Nielsen Mostafa Mehdipour Ghazi
Deep learning-based models in medical imaging often struggle to generalize
effectively to new scans due to data heterogeneity arising from differences in
hardware, acquisition parameters, population, and artifacts. This limitation
presents a significant challenge in adopting machine learning models for
clinical practice. We propose an unsupervised method for robust domain
adaptation in brain MRI segmentation by leveraging MRI-specific augmentation
techniques. To evaluate the effectiveness of our method, we conduct extensive
experiments across diverse datasets, modalities, and segmentation tasks,
comparing against the state-of-the-art methods. The results show that our
proposed approach achieves high accuracy, exhibits broad applicability, and
showcases remarkable robustness against domain shift in various tasks,
surpassing the state-of-the-art performance in the majority of cases.