ArticleJournal of biomedical informatics2026
On the transfer learning behavior of domain-specific vision-language models in screening mammography.
Article in Journal of biomedical informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Biomedical multimodal large language models: From model-centric development to clinically grounded evaluation and integration.Journal of biomedical informatics · 2026Article
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7 authors.
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Abstract
Vision-Language models have shown remarkable performance for natural images and text. Given the homology of the anatomy, high gray-scale image dimension, and the unbalanced datasets, the traditional VLMs do not adapt well to radiological applications. In this work, we empirically adapted image encoder trained within domain-specific VLMs to be applied in two downstream tasks for 2D mammogram image analysis: tissue density estimation and BI-RADS prediction. We study the transfer learning behavior using linear probing, fine-tuning, and online self distillation. We analyze that knowledge driven domain-specific VLM backbones with frozen weights perform better than MammoClip VLM model as well as supervised baselines such as ViT and CNNs even with only 5% of training data. Generalization capabilities are further studied of these models on two external datasets.
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