Self-supervised learning and transformer-based technologies in breast cancer imaging

Abstract

Breast cancer is the most common malignancy among women worldwide, and imaging remains critical for early detection, diagnosis, and treatment planning. Recent advances in artificial intelligence (AI), particularly self-supervised learning (SSL) and transformer-based architectures, have opened new opportunities for breast image analysis. SSL offers a label-efficient strategy that reduces reliance on large annotated datasets, with evidence suggesting that it can achieve strong performance. Transformer-based architectures, such as Vision Transformers, capture long-range dependencies and global contextual information, complementing the local feature sensitivity of convolutional neural networks. This study provides a comprehensive overview of recent developments in SSL and transformer models for breast lesion segmentation, detection, and classification, highlighting representative studies in each domain. It also discusses the advantages and current limitations of these approaches and outlines future research priorities, emphasizing that successful clinical translation depends on access to multi-institutional datasets to ensure generalizability, rigorous external validation to confirm real-world performance, and interpretable model designs to foster clinician trust and enable safe, effective deployment in clinical practice.