MedMAE: A Self-Supervised Backbone for Medical Imaging Tasks

TL;DR

MedMAE introduces a self-supervised pre-trained backbone trained on a large-scale unlabeled medical image dataset, significantly improving performance across various medical imaging tasks compared to models pre-trained on natural images.

Contribution

The paper presents a novel self-supervised pre-training approach using Masked Autoencoder on a large medical image dataset, addressing domain shift issues in medical imaging.

Findings

Outperforms existing pre-trained models on multiple medical imaging tasks.

Demonstrates the effectiveness of self-supervised learning for medical image representation.

Provides a new large-scale medical image dataset for pre-training.

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.