Efficient Distributed Vision Transformer Foundation Model for Medical Imaging through Random Masked Sampling

In spite of the recent success of deep learning in the medical domain, the problem of data scarcity in the medical domain gets aggravated due to privacy and data ownership issues. Distributed learning approaches including federated learning have been studied to alleviate the problems, but they suffer from cumbersome communication overheads and weakness in privacy protection. To address this, here we propose a self-supervised masked sampling distillation method for vision transformer that can be performed without continuous communication but still enhance privacy using a vision transformer-specific encryption method. The effectiveness of our method is demonstrated with extensive experiments on two medical domain data and two different downstream tasks, showing superior performances than those obtained with the existing distributed learning strategy as well as the fine-tuning only baseline. As the self-supervised model built with the proposed method is capable of having a general semantic understanding of the modality, we demonstrate its potential as a task-agnostic foundation model for various medical tasks, widening the applicability in the medical domain.