Self-Supervised Learning Framework for Medical Data Representation in Low-Resource Healthcare Environments

Abstract

The scarcity of labeled medical data presents a major challenge in developing robust healthcare predictive models, particularly in low-resource environments. This paper presents a Self-Supervised Learning Framework for Medical Data Representation aimed at leveraging large volumes of unlabeled healthcare data to improve model performance. The proposed system utilizes self-supervised learning techniques to learn meaningful representations from medical images, clinical text, and physiological signals without requiring extensive manual annotation. Pretext tasks such as contrastive learning and masked data prediction are employed to capture underlying data structures. These learned representations are then fine-tuned for downstream tasks such as disease classification and risk prediction. The framework is integrated with deep learning architectures to enhance feature extraction and generalization. Experimental results demonstrate that the proposed approach significantly improves performance in scenarios with limited labeled data. Additionally, integration with prior research in healthcare analytics strengthens system robustness and adaptability. The study highlights the potential of self-supervised learning in enabling scalable and efficient healthcare solutions, especially in resource-constrained settings.