MedMAE: A Self-Supervised Backbone for Medical Imaging Tasks

• URL: http://arxiv.org/abs/2407.14784v1
• Date: Sat, 20 Jul 2024 07:29:04 GMT
• Title: MedMAE: A Self-Supervised Backbone for Medical Imaging Tasks
• Authors: Anubhav Gupta, Islam Osman, Mohamed S. Shehata, John W. Braun,
• Abstract summary: We propose a large-scale unlabeled dataset of medical images and a backbone pre-trained 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.
• Score: 3.1296917941367686
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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