  The volume of open-source biomedical data has been essential to the
development of various spheres of the healthcare community since more `free'
data can provide individual researchers more chances to contribute. However,
institutions often hesitate to share their data with the public due to the risk
of data exploitation by unauthorized third parties for another commercial usage
(e.g., training AI models). This phenomenon might hinder the development of the
whole healthcare research community. To address this concern, we propose a
novel approach termed `unlearnable biomedical image' for protecting biomedical
data by injecting imperceptible but delusive noises into the data, making them
unexploitable for AI models. We formulate the problem as a bi-level
optimization and propose three kinds of anti-learning perturbation generation
approaches to solve the problem. Our method is an important step toward
encouraging more institutions to contribute their data for the long-term
development of the research community.
