X-ray modalities in the era of artificial intelligence: overview of self-supervised learning approach

Abstract

Self-supervised learning enables the creation of algorithms that outperform supervised pre-training methods in numerous computer vision tasks. This paper provides a comprehensive overview of self-supervised learning applications across various X-ray modalities, including conventional X-ray, computed tomography, mammography, and dental X-ray. Apart from the application of self-supervised learning in the interpretation phase of X-ray images, the paper also emphasizes the critical role of self-supervised learning integration in the preprocessing and archiving phase. Furthermore, the paper explores the application of self-supervised learning in multi-modal scenarios, which represents a key future direction in developing machine learning-based applications across the field of medicine. Lastly, the paper addresses the main challenges associated with the development of self-supervised learning applications tailored for X-ray modalities. The findings from the reviewed literature strongly suggest that the self-supervised learning approach has the potential to be a “game-changer”, enabling the elimination of the current situation where many machine learning-based systems are developed but few are deployed in daily clinical practice.

Keywords

• self-supervised learning
• X-ray modalities
• generative models
• contrastive models
• multi-modal scenarios