  Data sharing in the medical image analysis field has potential yet remains
underappreciated. The aim is often to share datasets efficiently with other
sites to train models effectively. One possible solution is to avoid
transferring the entire dataset while still achieving similar model
performance. Recent progress in data distillation within computer science
offers promising prospects for sharing medical data efficiently without
significantly compromising model effectiveness. However, it remains uncertain
whether these methods would be applicable to medical imaging, since medical and
natural images are distinct fields. Moreover, it is intriguing to consider what
level of performance could be achieved with these methods. To answer these
questions, we conduct investigations on a variety of leading data distillation
methods, in different contexts of medical imaging. We evaluate the feasibility
of these methods with extensive experiments in two aspects: 1) Assess the
impact of data distillation across multiple datasets characterized by minor or
great variations. 2) Explore the indicator to predict the distillation
performance. Our extensive experiments across multiple medical datasets reveal
that data distillation can significantly reduce dataset size while maintaining
comparable model performance to that achieved with the full dataset, suggesting
that a small, representative sample of images can serve as a reliable indicator
of distillation success. This study demonstrates that data distillation is a
viable method for efficient and secure medical data sharing, with the potential
to facilitate enhanced collaborative research and clinical applications.
