Self-supervised learning for small-scale medical imaging dataset

In recent years, advancements in deep learning have dramatically improved performance in medical image analysis, yet these models typically rely on large-scale labeled datasets, which are often unattainable in medical settings due to privacy concerns, limited data availability, and high annotation costs. This study explores the application of self-supervised learning (SSL) techniques to overcome these limitations and effectively utilize small-scale medical imaging datasets. By leveraging SSL, we enable models to learn useful feature representations without requiring extensive labeled data. We investigate various self-supervised approaches, including contrastive learning and masked image modeling, and evaluate their effectiveness on a limited dataset of medical images. Our experiments demonstrate that SSL-based models can achieve competitive performance, even when trained on a fraction of the labeled data typically required for supervised methods. Additionally, we explore the impact of SSL on model robustness and generalization across diverse medical imaging modalities. The findings suggest that self-supervised techniques could reduce dependency on annotated data, paving the way for broader, more scalable applications in medical imaging. This research contributes to the development of efficient, scalable diagnostic tools that can be deployed in data-constrained environments, potentially improving diagnostic accuracy and accessibility in smaller healthcare facilities.