🤖 AI Summary
Existing self-supervised methods for chest X-ray (CXR) analysis are often limited by their reliance on strong data augmentations, sensitivity to non-diagnostic background regions, or high computational costs. This work proposes Semantic-aware Patch-based Contrastive Learning (S-PCL), which constructs an intrinsic information bottleneck by randomly partitioning image patches from a single CXR into two complementary yet incomplete semantic subsets. This design compels the encoder to infer global anatomical and pathological structures from local cues, thereby modeling long-range dependencies and structural consistency. Notably, S-PCL operates without handcrafted augmentations, momentum encoders, or auxiliary decoders. It achieves state-of-the-art accuracy on benchmark datasets such as ChestX-ray14 and CheXpert while significantly reducing computational overhead—requiring the lowest GFLOPs among current self-supervised approaches.
📝 Abstract
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 partitions patch tokens from a single CXR into two non-overlapping semantic subsets. Each subset provides a complementary but incomplete view. The encoder must maximize agreement between these partitions, implicitly inferring global anatomical layout and local pathological cues from partial evidence. This semantic partitioning forms an internal bottleneck that enforces long-range dependency modeling and structural coherence. S-PCL eliminates the need for hand-crafted augmentations, auxiliary decoders, and momentum encoders. The resulting architecture is streamlined, computationally efficient, and easy to scale. Extensive experiments on large-scale CXR benchmarks, including ChestX-ray14, CheXpert, RSNA Pneumonia and SIIM-ACR Pneumothorax, show that S-PCL achieves competitive performance while attaining the lowest GFLOPs and superior accuracy among existing SSL approaches.