Task-Aligned Self-Supervised Learning for Medical Image Analysis: A Systematic Review and Practical Design Guidelines

A systematic review of self-supervised learning (SSL) in medical imaging analyzes 75 studies to establish that SSL effectiveness depends on alignment between pretext task design, imaging modality, and clinical objectives. The research provides practical guidelines showing contrastive methods excel at classification while generative approaches better support segmentation, with no universal optimal strategy.

This systematic review addresses a critical challenge in medical AI: reducing annotation burden while maintaining clinical relevance. The researchers examined 75 studies across four SSL paradigms to understand how pretext task design influences downstream performance. Their finding that no single SSL approach universally outperforms others challenges the tendency in machine learning to seek one-size-fits-all solutions. Instead, the research demonstrates that task-objective alignment is paramount—contrastive learning excels at extracting global discriminative features useful for classification but may miss subtle pathological patterns critical for diagnosis. Generative and spatial prediction methods preserve local anatomical structure, making them superior for segmentation and dense prediction tasks requiring pixel-level accuracy. This distinction matters because medical imaging spans diverse applications from disease classification to lesion delineation, each with different feature requirements. The review highlights that SSL provides maximal benefit in low-label and few-shot scenarios, directly addressing real-world constraints where annotated medical data remains expensive and scarce. The identification of modality-specific design requirements suggests that SSL frameworks for ultrasound differ fundamentally from those for CT or MRI. The outlined open challenges—pathology-aware pretext task design, resource efficiency for high-dimensional data, and standardized evaluation protocols—represent concrete research directions. For AI developers and healthcare organizations, these guidelines transform SSL from a generic technique into a strategic design problem requiring domain expertise. The emphasis on clinical relevance and practical implementation differentiates this work from purely architectural contributions, positioning it as a valuable resource for translating SSL research into clinical applications.

• →Self-supervised learning effectiveness depends critically on alignment between pretext task design, imaging modality, and downstream clinical objectives.
• →Contrastive methods excel at classification by learning global features but may miss subtle pathological patterns important for diagnosis.
• →Generative and spatial prediction approaches better preserve local anatomical structure, making them superior for segmentation and dense prediction tasks.
• →Self-supervised learning provides greatest benefit in low-label and few-shot regimes where annotated data is scarce and expensive.
• →Modality-specific design is essential, with different SSL strategies optimally suited for ultrasound, CT, MRI, and other imaging types.