Contrastive Self-Supervised Learning in Medical Signal Processing: Literature Review and Case Study

Abstract

the ever-increasing volume and complexity of medical data necessitate innovative approaches for effective signal processing and analysis. This paper explores the potential of Self-Supervised Learning (SSL) as a paradigm shift in the domain of medical signal processing. Traditional supervised techniques frequently depend on labeled datasets, which are challenging to obtain in the medical domain due to privacy concerns and the need for expert annotations. SSL offers a promising alternative by leveraging inherent structures within the data to generate supervision signals. Our exploration underscores the potential of SSL as a transformative paradigm for handling large-scale, unlabeled medical datasets. As we move towards more autonomous and data-driven healthcare systems, the adoption of SSL in medical signal processing emerges as a pivotal step, offering the promise of improved diagnostic accuracy, scalability, and reduced reliance on labeled data. This paper contributes to the ongoing discourse on the integration of SSL techniques into the medical domain, fostering advancements in signal processing methodologies that can ultimately enhance patient care and medical research.