Hyperparameter-Free Medical Image Synthesis for Sharing Data and
  Improving Site-Specific Segmentation
Alexander Chebykin Peter A. N. Bosman Tanja Alderliesten
Sharing synthetic medical images is a promising alternative to sharing real
images that can improve patient privacy and data security. To get good results,
existing methods for medical image synthesis must be manually adjusted when
they are applied to unseen data. To remove this manual burden, we introduce a
Hyperparameter-Free distributed learning method for automatic medical image
Synthesis, Sharing, and Segmentation called HyFree-S3. For three diverse
segmentation settings (pelvic MRIs, lung X-rays, polyp photos), the use of
HyFree-S3 results in improved performance over training only with site-specific
data (in the majority of cases). The hyperparameter-free nature of the method
should make data synthesis and sharing easier, potentially leading to an
increase in the quantity of available data and consequently the quality of the
models trained that may ultimately be applied in the clinic. Our code is
available at https://github.com/AwesomeLemon/HyFree-S3