  Medical imaging tasks are very challenging due to the lack of publicly
available labeled datasets. Hence, it is difficult to achieve high performance
with existing deep-learning models as they require a massive labeled dataset to
be trained effectively. An alternative solution is to use pre-trained models
and fine-tune them using the medical imaging dataset. However, all existing
models are pre-trained using natural images, which is a completely different
domain from that of medical imaging, which leads to poor performance due to
domain shift. To overcome these problems, we propose a large-scale unlabeled
dataset of medical images and a backbone pre-trained using the proposed dataset
with a self-supervised learning technique called Masked autoencoder. This
backbone can be used as a pre-trained model for any medical imaging task, as it
is trained to learn a visual representation of different types of medical
images. To evaluate the performance of the proposed backbone, we used four
different medical imaging tasks. The results are compared with existing
pre-trained models. These experiments show the superiority of our proposed
backbone in medical imaging tasks.
