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

Prakhar Bhardwaj; Sheethal Bhat; Andreas Maier

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arXiv · 2503.13134

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

Prakhar Bhardwaj·Sheethal Bhat·Andreas Maier

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Abstract

Due to the large volume of medical imaging data, advanced AI methodologies are needed to assist radiologists in diagnosing thoracic diseases from chest X-rays (CXRs). Existing deep learning models often require large, labeled datasets, which are scarce in medical imaging due to the time-consuming and expert-driven annotation process. In this paper, we extend the existing approach to enhance zero-shot learning in medical imaging by integrating Contrastive Language-Image Pre-training (CLIP) with Momentum Contrast (MoCo), resulting in our proposed model, MoCoCLIP. Our method addresses challenges posed by class-imbalanced and unlabeled datasets, enabling improved detection of pulmonary pathologies. Experimental results on the NIH ChestXray14 dataset demonstrate that MoCoCLIP outperforms the state-of-the-art CheXZero model, achieving relative improvement of approximately 6.5%. Furthermore, on the CheXpert dataset, MoCoCLIP demonstrates superior zero-shot performance, achieving an average AUC of 0.750 compared to CheXZero with 0.746 AUC, highlighting its enhanced generalization capabilities on unseen data.

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Prakhar Bhardwaj, Sheethal Bhat, Andreas Maier. 2025-03-17. Enhancing zero-shot learning in medical imaging: integrating clip with advanced techniques for improved chest x-ray analysis. https://arxiv.org/abs/2503.13134

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