Learning medical visual representations through vision-language pre-training has reached remarkable progress. Despite the promising performance, it still faces challenges, i.e., local alignment lacks interpretability and clinical relevance, and the insufficient internal and external representation learning of image-report pairs. To address these issues, we propose an Anatomical Structure-Guided (ASG) framework. Specifically, we parse raw reports into triplets <anatomical region, finding, existence>, and fully utilize each element as supervision to enhance representation learning. For anatomical region, we design an automatic anatomical region-sentence alignment paradigm in collaboration with radiologists, considering them as the minimum semantic units to explore fine-grained local alignment. For finding and existence, we regard them as image tags, applying an image-tag recognition decoder to associate image features with their respective tags within each sample and constructing soft labels for contrastive learning to improve the semantic association of different image-report pairs. We evaluate the proposed ASG framework on two downstream tasks, including five public benchmarks. Experimental results demonstrate that our method outperforms the state-of-the-art methods.

通过视觉语言预训练学习医学视觉表示已取得显著进展，本研究提出了一种以解剖结构为指导的框架（ASG），以解决局部对齐的可解释性和临床相关性不足，以及图像-报告对的内外表示学习不足的问题。通过自动解剖句子对齐，并将发现和存在视为图像标签，该方法在五个公共基准数据集上展示出优于现有方法的实验结果。

解剖结构导向的医学视觉语言预训练