Abstract
Manual annotation of medical image datasets is labor-intensive and prone to biases. Moreover, the rate at which image data accumulates significantly outpaces the speed of manual annotation, posing a challenge to the advancement of machine learning, particularly in the realm of supervised learning. Self-supervised learning is an emerging field that capitalizes on unlabeled data for training, thereby circumventing the need for extensive manual labeling. This learning paradigm generates synthetic pseudo-labels through pretext tasks, compelling the network to acquire image representations in a pseudo-supervised manner and subsequently fine-tuning with a limited set of annotated data to achieve enhanced performance. This review begins with an overview of prevalent types and advancements in self-supervised learning, followed by an exhaustive and systematic examination of methodologies within the medical imaging domain from 2018 to September 2024. The review encompasses a range of medical image modalities, including CT, MRI, X-ray, Histology, and Ultrasound. It addresses specific tasks, such as Classification, Localization, Segmentation, Reduction of False Positives, Improvement of Model Performance, and Enhancement of Image Quality. The analysis reveals a descending order in the volume of related studies, with CT and MRI leading the list, followed by X-ray, Histology, and Ultrasound. Except for CT and MRI, there is a greater prevalence of studies focusing on contrastive learning methods over generative learning approaches. The performance of MRI/Ultrasound classification and all image types segmentation still has room for further exploration. Generally, this review can provide conceptual guidance for medical professionals to combine self-supervised learning with their research.