Locating pathologies automatically from medical images aids the understanding
of the emergence and progression of diseases, and such an ability can
significantly benefit clinical diagnostics. However, existing deep learning
models heavily rely on expert annotations and lack generalization capabilities
in open clinical environments. In this study, we present a generalizable
vision-language pre-training model for Annotation-Free pathology Localization
(AFLoc). The core strength of AFLoc lies in its image annotation-free
multi-level semantic structure-based contrastive learning, which
comprehensively aligns multi-granularity medical concepts from reports with
abundant image features, to adapt to the diverse expressions of observed and
emerging unseen pathologies. We conducted extensive experimental validation
across 4 distinct external datasets, encompassing 11 types of chest
pathologies, to verify its generalization ability. The results demonstrate that
AFLoc surpasses 6 state-of-the-art methods and even outperforms the human
benchmark in locating 5 different pathologies, underscoring its suitability for
complex clinical environments.

该研究提出了一种适用于医学图像的注释免费病理定位 (AFLoc) 的通用视觉语言预训练模型，通过多层语义结构对齐医学报告中的多粒度医学概念与丰富图像特征，以适应观察到的和新出现的不同病理表达方式，验证了其在复杂临床环境中的适用性。