\section{Discussion \& Conclusion} \label{sec:dis}

We have introduced HyFree-S3, a method for synthesizing medical images for sharing and segmentation that does not require adjusting hyperparameters, as it relies on our hyperparameter-free setup of StyleGAN2 for image generation and similarly hyperparameter-free nnU-Net~\cite{isensee_automated_2021} for segmentation.

The method is asynchronous and does not expect different sites to share infrastructure or to be online simultaneously. HyFree-S3 was shown to come close to the performance of training with centralized real data and to improve upon training only with local data, while not requiring sharing real patient data. The synthetic dataset produced by HyFree-S3 could be reused for training future models without going back to the sites where the real data is stored. We additionally proposed a memorization evaluation technique for ensuring that synthetic data is sufficiently dissimilar from real data.

However, our method also has a number of limitations. Firstly, as models for data sharing are trained on each site independently, large enough datasets have to be present there (especially for training a GAN). Secondly, we generate images and annotations separately for reasons described in \sectionref{met:sep-gen-seg}, which nonetheless means that the annotations produced by an independent model will not perfectly correspond to the images, introducing noise to synthetic data. Finally, our method as presented cannot be applied to all possible datasets, as it relies on nnU-Net for preprocessing and segmentation: nnU-Net can handle a variety of inputs but not, e.g., high-resolution histopathology images. However, extending nnU-Net with an appropriate data preprocessing procedure would make HyFree-S3 applicable too.

Despite some limitations, adaptable methods that do not require task-specific tuning are still valuable for the translation of deep learning models into the real world, where there may not be time or expertise for careful manual adaptation to each task.