This research introduces MoCoCLIP, a novel approach that combines contrastive language-image pre-training with advanced techniques to enable effective diagnosis of thoracic diseases without requiring large labeled datasets.

• Addresses the critical challenge of limited labeled data in medical imaging
• Leverages zero-shot learning to detect pulmonary pathologies in chest X-rays
• Demonstrates improved performance when evaluated on NIH ChestXray14 and CheXpert datasets
• Offers a practical solution to assist radiologists with the growing volume of medical imaging data

This innovation matters for healthcare because it reduces dependency on expert annotations, potentially accelerating diagnostic workflows and improving accessibility of AI-assisted medical imaging analysis in resource-constrained settings.

Enhancing zero-shot learning in medical imaging: integrating clip with advanced techniques for improved chest x-ray analysis