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

A systematic review of self-supervised learning in medical imaging finds that no single method works best for all tasks, and that aligning the training strategy with the specific clinical goal and imaging modality is critical for performance, according to a study published on arXiv [1]. The review, conducted by Kanakka Hewage Chathura Thimanka Wimalasiri, analyzed 75 studies published between 2017 and 2025 following PRISMA guidelines [1][2]. It organizes self-supervised learning (SSL) approaches into four paradigms: contrastive, non-contrastive and predictive, generative and reconstruction-based, and hybrid learning [1]. The analysis maps each paradigm to the downstream objectives it best supports, moving beyond a simple catalog of architectures [2]. A key finding is that contrastive methods, which learn global discriminative features, align well with classification tasks but may overlook subtle pathological patterns [1][2]. For segmentation and other dense prediction tasks, generative and spatial prediction-based approaches are more suitable because they better preserve local anatomical structure [1]. Hybrid methods, which combine elements of different paradigms, offered the most balanced performance across tasks [1][2]. The study emphasizes that modality-specific design is critical and that SSL provides its greatest benefit in low-label and few-shot regimes, where annotated data is scarce [1][2]. The work distills these findings into practical design guidelines and outlines open challenges, including the need for pathology-aware pretext task design, resource-efficient training for high-dimensional data, and standardized evaluation protocols [1][2]. The review addresses a persistent bottleneck in medical artificial intelligence. AI systems, which are the capability of computational systems to perform tasks like learning and perception, often require large, labeled datasets to function effectively [5]. In medical imaging, expert annotation is expensive and time-consuming, making techniques that learn from unlabeled data particularly valuable [1]. However, the study cautions that the effectiveness of SSL depends heavily on the design of the pretext task and its alignment with the downstream clinical objective [2]. Concerns about bias in AI systems, which can emerge from how data is coded, collected, or selected to train an algorithm, underscore the importance of careful design in medical applications [3]. Algorithmic bias has been cited in healthcare contexts, compounding existing disparities, and issues can stem from imbalanced datasets [3]. The review's call for pathology-aware pretext tasks and standardized evaluation protocols aligns with broader efforts to create more reliable and fair AI systems in clinical settings [1][3].