The integration of artificial intelligence (AI) with radiology has marked a
transformative era in medical diagnostics. Vision foundation models have been
adopted to enhance radiologic imaging analysis. However, the distinct
complexities of radiological imaging, including the interpretation of 2D and 3D
radiological data, pose unique challenges that existing models, trained on
general non-medical images, fail to address adequately. To bridge this gap and
capitalize on the diagnostic precision required in medical imaging, we
introduce RadCLIP: a pioneering cross-modal foundational model that harnesses
Contrastive Language-Image Pre-training (CLIP) to refine radiologic image
analysis. RadCLIP incorporates a novel 3D slice pooling mechanism tailored for
volumetric image analysis and is trained using a comprehensive and diverse
dataset of radiologic image-text pairs. Our evaluations demonstrate that
RadCLIP effectively aligns radiological images with their corresponding textual
annotations, and in the meantime, offers a robust vision backbone for
radiologic imagery with significant promise.

人工智能与放射学的整合标志着医学诊断的一个转型时代，采用视觉基础模型改进放射学图像分析，引入 RadCLIP 用于优化放射学图像分析，包括创新的三维切片池化机制和多样的放射学图像 - 文本数据集，评估结果显示 RadCLIP 可以有效对齐放射学图像与对应的文本标注，并提供强大的视觉支持。