MedMAE: A Self-Supervised Backbone for Medical Imaging Tasks
<|reference_start|>MedMAE: A Self-Supervised Backbone for Medical Imaging Tasks: 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.<|reference_end|>
@article{gupta2024medmae:,
    title={MedMAE: A Self-Supervised Backbone for Medical Imaging Tasks},
    author={Anubhav Gupta, Islam Osman, Mohamed S. Shehata, and John W. Braun},
    journal={arXiv preprint arXiv:2407.14784},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.14784},
    primaryClass={eess.IV cs.CV}
}