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

02/27/2023
by   Xiaoman Zhang, et al.
9

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.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset