  Generalization is one of the key challenges in the clinical validation and
application of deep learning models to medical images. Studies have shown that
such models trained on publicly available datasets often do not work well on
real-world clinical data due to the differences in patient population and image
device configurations. Also, manually annotating clinical images is expensive.
In this work, we propose an unsupervised domain adaptation (UDA) method using
Cycle-GAN to improve the generalization ability of the model without using any
additional manual annotations.
