Systematic Review of Task-Aligned Self-Supervised Learning in Medical Imaging
A systematic review examining 75 studies published between 2017 and 2025 has been released, focusing on task-aligned self-supervised learning (SSL) in medical imaging. Organized into four paradigms—contrastive, non-contrastive and predictive, generative and reconstruction-based, and hybrid learning—the review adheres to PRISMA guidelines. It correlates each paradigm with downstream objectives, such as classification, segmentation, and detection. Notably, there is no single optimal SSL strategy; effectiveness varies based on task alignment. The study also offers design guidelines for SSL in medical imaging and is accessible on arXiv under ID 2605.23995.
Key facts
- The review analyzes 75 studies published between 2017 and 2025.
- SSL methods are organized into four paradigms: contrastive, non-contrastive and predictive, generative and reconstruction-based, and hybrid learning.
- The review follows PRISMA guidelines.
- The study maps each paradigm to downstream objectives like classification, segmentation, and detection.
- No universally optimal SSL strategy exists; performance depends on task alignment.
- The review provides practical design guidelines for SSL in medical imaging.
- The paper is available on arXiv with ID 2605.23995.
- The review is task-oriented, focusing on pretext task design and clinical objective alignment.
Entities
Institutions
- arXiv