UniMed-CLIP: Towards a Unified Image-Text Pretraining Paradigm for Diverse Medical Imaging Modalities

Understand

Vision-Language Models (VLMs) trained via contrastive learning have achieved notable success in natural image tasks.

• However, their application in the medical domain remains limited due to the scarcity of openly accessible, large-scale medical image-text datasets.
• Existing medical VLMs either train on closed-source proprietary or relatively small open-source datasets that do not generalize well.
• Similarly, most models remain specific to a single or limited number of medical imaging domains, again restricting their applicability to other modalities.