Knowledge-enhanced Visual-Language Pre-training on Chest Radiology Images

• URL: http://arxiv.org/abs/2302.14042v3
• Date: Wed, 14 Jun 2023 07:33:16 GMT
• Title: Knowledge-enhanced Visual-Language Pre-training on Chest Radiology Images
• Authors: Xiaoman Zhang, Chaoyi Wu, Ya Zhang, Yanfeng Wang, Weidi Xie
• Abstract summary: We propose Knowledge-enhanced Auto Diagnosis (KAD) to guide vision-supervised pre-training using paired chest X-rays and radiology reports. We evaluate KAD on four external X-ray datasets and demonstrate that its zero-shot performance is superior to that of fully-language models.
• Score: 40.52487429030841
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While multi-modal foundation models pre-trained on large-scale data have been successful in natural language understanding and vision recognition, their use in medical domains is still limited due to the fine-grained nature of medical tasks and the high demand for domain knowledge. To address this challenge, we propose a novel approach called Knowledge-enhanced Auto Diagnosis (KAD) which leverages existing medical domain knowledge to guide vision-language pre-training using paired chest X-rays and radiology reports. We evaluate KAD on {four} external X-ray datasets and demonstrate that its zero-shot performance is not only comparable to that of fully-supervised models, but also superior to the average of three expert radiologists for three (out of five) pathologies with statistical significance. Moreover, when few-shot annotation is available, KAD outperforms all existing approaches in fine-tuning settings, demonstrating its potential for application in different clinical scenarios.

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