  Defining pathologies automatically from medical images aids the understanding
of the emergence and progression of diseases, and such an ability is crucial in
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 model
for Annotation-Free pathology Localization (AFLoc). The core strength of AFLoc
lies in its extensive 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
pathologies and unseen pathologies without the reliance on image annotations
from experts. We demonstrate the proof of concept on Chest X-ray images, with
extensive experimental validation across 6 distinct external datasets,
encompassing 13 types of chest pathologies. The results demonstrate that AFLoc
surpasses state-of-the-art methods in pathology localization and
classification, and even outperforms the human benchmark in locating 5
different pathologies. Additionally, we further verify its generalization
ability by applying it to retinal fundus images. Our approach showcases AFLoc's
versatilities and underscores its suitability for clinical diagnosis in complex
clinical environments.
