Efficient Chest X-ray Representation Learning via Semantic-Partitioned Contrastive Learning
Self-supervised learning (SSL) has emerged as a powerful paradigm for Chest X-ray (CXR) analysis under limited annotations. Yet, existing SSL strategies remain suboptimal for medical imaging. Masked image modeling allocates substantial computation to reconstructing high-frequency background details with limited diagnostic value. Contrastive learning, on the other hand, often depends on aggressive augmentations that risk altering clinically meaningful structures. We introduce Semantic-Partitioned Contrastive Learning (S-PCL), an efficient pre-training framework tailored for CXR representation learning. Instead of reconstructing pixels or relying on heavy augmentations, S-PCL randomly parti...
| Authors | Wangyu Feng, Shawn Young, Lijian Xu |
|---|---|
| Primary category | cs.CV |
| Published | 7 March 2026 |
| Last updated at source | 24 March 2026 |
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