Foundation models, trained on vast amounts of data using self-supervised techniques, have emerged as a promising frontier for advancing artificial intelligence (AI) applications in medicine. This study evaluates three different vision-language foundation models (RAD-DINO, CheXagent, and BiomedCLIP) on their ability to capture fine-grained imaging features for radiology tasks. The models were assessed across classification, segmentation, and regression tasks for pneumothorax and cardiomegaly on chest radiographs. Self-supervised RAD-DINO consistently excelled in segmentation tasks, while text-supervised CheXagent demonstrated superior classification performance. BiomedCLIP showed inconsistent performance across tasks. A custom segmentation model that integrates global and local features substantially improved performance for all foundation models, particularly for challenging pneumothorax segmentation. The findings highlight that pre-training methodology significantly influences model performance on specific downstream tasks. For fine-grained segmentation tasks, models trained without text supervision performed better, while text-supervised models offered advantages in classification and interpretability. These insights provide guidance for selecting foundation models based on specific clinical applications in radiology.

本研究解决了放射学中特征捕获的不足，评估了三种视觉语言基础模型（RAD-DINO、CheXagent 和 BiomedCLIP）在胸部X光片的气胸和心脏肥大任务中的表现。研究发现，自监督的RAD-DINO在分割任务上表现突出，而文本监督的CheXagent在分类上更优，结合全局和局部特征的自定义分割模型显著提高了所有基础模型的表现，提供了选择基础模型的实用指导。

评估放射学中的视觉语言模型（VLMs）：全面分析