When Vision-Language Models Meet Fetal Cardiac Ultrasound: Dual-Level Contrastive Learning for Out-of-Distribution Detection
Recent advances in vision-language models (VLMs) have shown remarkable performance in medical image classification tasks. However, applying VLMs to fetal cardiac ultrasound (FCU) remains challenging due to compound distribution shifts, including covariate shifts caused by cross-center heterogeneity and semantic shifts arising from clinically non-standard views. To address this issue, we propose Dual-Level Contrastive Learning (DLCL), the first prompt-based VLM framework for out-of-distribution (OOD) detection in FCU, to the best of our knowledge. DLCL explicitly shapes the vision-language representation space through complementary local-level and global-level contrastive objectives. Specifically, local contrastive learning aligns instance-level features to mitigate covariate shifts, whereas global contrastive learning regularizes global prototypes to address semantic shifts. We conduct extensive experiments on a private multi-center FCU dataset and the public ISIC-OOD dataset to validate the proposed approach. On the challenging FCU task, DLCL achieves an AUROC of 89.61% and a harmonic mean of 80.35%, significantly outperforming state-of-the-art methods.