CLIP-XRad: Learning Multimodal Representations of Chest X-Rays Through Contrastive Pretraining with Medical Concept Alignment

Contrastive vision-language pretraining has shown strong performance in learning rich multimodal representations from image-text pairs. However, applying this paradigm to the medical domain presents significant challenges due to the complex visual patterns of medical conditions and the subtle, often ambiguous language used in radiology reports. To address these limitations, we propose CLIP-XRad, a weakly-supervised contrastive vision-language model specialized in chest X-ray interpretation. Our method introduces medical concept alignment into the contrastive learning process via a custom training objective that incorporates both instance-level and concept-level similarity, guiding the model to structure its embedding space around shared clinical semantics. Through extensive experiments, we demonstrate that CLIP-XRad achieves competitive performance in image-text retrieval, while also transferring effectively to downstream tasks such as multi-label classification and report generation. These results demonstrate that concept-aware contrastive pretraining can produce generalizable, semantically meaningful medical representations that provide a data-efficient solution for clinical applications.