Self-Supervised Learning for Medical Imaging: From Representation Learning to Clinical Translation

Medical imaging lies at the core of modern diagnosis, prognosis, and treatment planning across nearly all clinical specialties. However, the rapid growth of imaging data has not been matched by the availability of high-quality labeled datasets, which remain costly, time-consuming, and ethically constrained. Self-supervised learning (SSL) has emerged as a powerful paradigm that enables deep learning models to learn meaningful visual representations from large-scale unlabeled medical images, reducing dependence on expert annotations. Unlike conventional supervised learning, SSL leverages intrinsic data structure through pretext tasks, contrastive objectives, and generative reconstruction to extract semantically rich features transferable across imaging modalities and clinical tasks. This article provides a comprehensive review of self-supervised learning methodologies for medical imaging, spanning representation learning, modality-specific architectures, and downstream clinical applications. We discuss recent advances in contrastive learning, masked image modeling, and multimodal SSL frameworks, highlighting their impact on disease detection, segmentation, prognosis, and workflow automation. Finally, we examine translational, ethical, and regulatory challenges—including robustness, interpretability, domain shift, and clinical trust—that will shape the integration of SSL-driven imaging systems into real-world healthcare practice.