AI RESEARCH
Efficient Chest X-ray Representation Learning via Semantic-Partitioned Contrastive Learning
arXiv CS.CV
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ArXi:2603.07113v1 Announce Type: new 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.