Task-Aligned Self-Supervised Learning for Medical Image Analysis: A Systematic Review and Practical Design Guidelines
Quick Take
This systematic review analyzes 75 studies on self-supervised learning (SSL) in medical imaging, emphasizing the importance of task alignment for effective performance. It categorizes SSL methods into four paradigms and highlights that no single strategy is optimal; rather, success hinges on aligning pretext tasks with clinical objectives and imaging modalities. Practical design guidelines are provided to enhance SSL frameworks in medical imaging.
Key Points
- Examines 75 studies published from 2017 to 2025 on SSL in medical imaging.
- Categorizes SSL methods into contrastive, generative, and hybrid paradigms.
- Highlights that performance is influenced by task alignment with imaging modalities.
- Generative methods excel in segmentation tasks by preserving local anatomical structures.
- Offers design guidelines and identifies challenges for future SSL research.
Article Content
From source RSS / original summaryarXiv:2605. 23995v1 Announce Type: new Abstract: Self-supervised learning (SSL) has emerged as a promising paradigm for addressing the annotation bottleneck in medical imaging by learning representations from unlabeled data. However, its effectiveness depends heavily on the design of the pretext task and its alignment with the downstream clinical objective.
We present a systematic, task-oriented review of SSL in medical imaging, examining how different pretext-task formulations influence performance across classification, segmentation, detection, and other tasks. Following PRISMA guidelines, we analyze 75 studies published between 2017 and 2025 and organize them into four paradigms: contrastive, non-contrastive and predictive, generative and reconstruction-based, and hybrid learning.
Rather than cataloguing methods by architecture, we map each paradigm to the downstream objectives it best supports. Our analysis shows there is no universally optimal SSL strategy; instead, performance is governed by the alignment between the pretext task, the imaging modality, and the target task. Contrastive methods learn global discriminative features and align well with classification, but may overlook subtle pathological patterns.
Generative and spatial prediction-based approaches better preserve local anatomical structure, making them more suitable for segmentation and other dense prediction tasks, while hybrid methods offer the most balanced performance. We further show that modality-specific design is critical and that SSL provides its greatest benefit in low-label and few-shot regimes.
Finally, we distill these findings into practical design guidelines and outline open challenges, including pathology-aware pretext task design, resource-efficient training for high-dimensional data, and standardized evaluation protocols. This work offers practical guidance for designing more effective and clinically relevant SSL frameworks in medical imaging.
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