Self-Supervised Pretraining and Sample Efficiency in Medical Imaging Classification

Abstract

The application of deep learning to medical imaging has transformed computational diagnostics, yet its efficacy is severely bottlenecked by the requirement for massive volumes of meticulously annotated datasets. Securing such expert annotations is prohibitively expensive, time-consuming, and prone to inter-observer variability. Self-supervised learning has emerged as a promising paradigm to circumvent this limitation by leveraging unlabelled data to learn robust, generalizable representations prior to fine-tuning on limited labeled cohorts. This paper presents a comprehensive benchmark study evaluating the sample efficiency and representational quality of various self-supervised pretraining architectures applied to diverse medical imaging classification tasks. By systematically comparing contrastive frameworks, momentum-based distillation paradigms, and non-contrastive methods against traditional supervised baselines initialized via natural image datasets, this study elucidates the specific contexts in which self-supervised representations excel. Extensive experiments are conducted across varying fractions of labeled data to rigorously quantify sample efficiency gains. The findings demonstrate that self-supervised pretraining significantly bridges the performance gap in extreme low-data regimes, frequently outperforming supervised natural image pretraining while capturing domain-specific semantic features critical for medical diagnostics. This research provides a foundational reference for deploying unsupervised representation learning in clinical environments where annotated data remains exceedingly scarce.