Contrastive learning with hard negative samples for chest X-ray multi-label classification

Contrastive learning has gained significant popularity and achieved remarkable success in learning meaningful representations in various domains. This study addresses the significant problem of dependency on labeled data in chest radiography (CXR) images, which are crucial for diagnosing respiratory diseases such as pneumonia but are both time-consuming and expensive to annotate. Despite extensive research, existing studies on CXR largely depend on labeled data. To overcome this challenge, we propose a framework named SURE (similarity, uncertainty, and representativeness) for multi-label classification with hard negatives in contrastive learning. The proposed framework incorporates these aspects when handling hard negatives and effectively combines contrastive learning and downstream tasks for robust representation learning and multi-label classification. Experimental validation using three distinct CXR datasets demonstrates that our approach significantly reduces the dependency on labeled data while achieving notable performance improvements over existing methods, highlighting its potential effectiveness and efficiency in the CXR domain.