Bharadwaj P, Bhat S, Maier A (2025)
Publication Language: English
Publication Status: Published
Publication Type: Other publication type
Future Publication Type: Other publication type
Publication year: 2025
Publisher: arXiv preprint arXiv:2503.13134
City/Town: arxiv
Open Access Link: https://arxiv.org/abs/2503.13134
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.
APA:
Bharadwaj, P., Bhat, S., & Maier, A. (2025). Enhancing zero-shot learning in medical imaging: integrating clip with advanced techniques for improved chest x-ray analysis. arxiv: arXiv preprint arXiv:2503.13134.
MLA:
Bharadwaj, Prakhar, Sheethal Bhat, and Andreas Maier. Enhancing zero-shot learning in medical imaging: integrating clip with advanced techniques for improved chest x-ray analysis. arxiv: arXiv preprint arXiv:2503.13134, 2025.
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