Foundation models trained via vision-language pretraining have demonstrated strong zero-shot capabilities across diverse image domains, yet their application to volumetric medical imaging remains limited. We introduce MedCT-VLM: Medical CT Vision-Language Model, a parameter-efficient vision-language framework designed to adapt large-scale CT foundation models for downstream clinical tasks. MedCT-VLM uses a parameter-efficient approach to adapt CT-CLIP, a contrastive vision-language model trained on 25,692 chest CT volumes, for multi-label pathology classification using Low-Rank Adaptation (LoRA). Rather than fine-tuning the model's 440 M parameters directly, we insert low-rank decomposition matrices into attention layers of both vision and text encoders, training only 1.67M parameters (0.38\% of total). We evaluate on zero-shot classification across 18 thoracic pathologies, where the model must align CT embeddings with unseen text prompts at inference without task-specific training. LoRA fine-tuning improves mean AUROC from 61.3\% to 68.9\% (+7.6 pp), accuracy from 67.2\% to 73.6\% (+6.4 pp), and macro-F1 from 32.1\% to 36.9\% (+4.8 pp). These results demonstrate that parameter-efficient methods can effectively transfer large-scale pretraining to downstream medical imaging tasks, particularly for zero-shot scenarios where labeled data is scarce.

翻译：通过视觉-语言预训练的基础模型已在多样化的图像领域展现出强大的零样本能力，但其在三维医学影像中的应用仍然有限。本文提出MedCT-VLM：医学CT视觉-语言模型，一种参数高效的视觉-语言框架，旨在将大规模CT基础模型适配至下游临床任务。MedCT-VLM采用参数高效方法，基于在25,692个胸部CT容积上训练的对比视觉-语言模型CT-CLIP，通过低秩适应（LoRA）技术实现多标签病理分类。我们未直接微调模型的4.4亿参数，而是在视觉与文本编码器的注意力层中插入低秩分解矩阵，仅训练167万参数（占总参数的0.38%）。我们在18种胸部病理的零样本分类任务上评估模型性能，要求模型在推理时无需任务特定训练即可将CT嵌入与未见过的文本提示对齐。LoRA微调将平均AUROC从61.3%提升至68.9%（+7.6个百分点），准确率从67.2%提升至73.6%（+6.4个百分点），宏观F1分数从32.1%提升至36.9%（+4.8个百分点）。这些结果表明，参数高效方法能有效将大规模预训练迁移至下游医学影像任务，尤其在标注数据稀缺的零样本场景中具有显著优势。