Knowledge-enhanced Pre-training for Auto-diagnosis of Chest Radiology Images

Despite of the success of multi-modal foundation models pre-trained on large-scale data in natural language understanding and vision recognition, its counterpart in medical and clinical domains remains preliminary, due to the fine-grained recognition nature of the medical tasks with high demands on domain knowledge. Here, we propose a knowledge-enhanced vision-language pre-training approach for auto-diagnosis on chest X-ray images. The algorithm, named Knowledge-enhanced Auto Diagnosis~(KAD), first trains a knowledge encoder based on an existing medical knowledge graph, i.e., learning neural embeddings of the definitions and relationships between medical concepts and then leverages the pre-trained knowledge encoder to guide the visual representation learning with paired chest X-rays and radiology reports. We experimentally validate KAD's effectiveness on three external X-ray datasets. The zero-shot performance of KAD is not only comparable to that of the fully-supervised models but also, for the first time, superior to the average of three expert radiologists for three (out of five) pathologies with statistical significance. When the few-shot annotation is available, KAD also surpasses all existing approaches in finetuning settings, demonstrating the potential for application in different clinical scenarios.