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arXiv AI By Haozhe Luo, Nirosh Sivanesan, Shelley Zixin Shu, Francesco Nitti, Mauricio Reyes

Meta-CLIP: Metadata-Conditioned Contrastive Learning for Zero-Shot Disease Inference

Read the original on arXiv AI →

Meta-CLIP is a metadata‑conditioned contrastive learning framework that incorporates DICOM acquisition information to reweight cross‑modal negatives during medical vision‑language pretraining. By defining a bounded pairwise margin over numerical and categorical DICOM fields, the method emphasizes distinctions among studies with similar imaging conditions while keeping the kernel fixed during optimization. Evaluations on chest radiography and volumetric CT show that Meta‑CLIP improves zero‑shot classification performance, achieving higher macro‑AUROC scores than baseline CLIP models on multiple benchmarks.

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