MedKLIP: Medical Knowledge Enhanced Language-Image Pre-Training in Radiology

• URL: http://arxiv.org/abs/2301.02228v3
• Date: Mon, 3 Apr 2023 09:57:51 GMT
• Title: MedKLIP: Medical Knowledge Enhanced Language-Image Pre-Training in Radiology
• Authors: Chaoyi Wu, Xiaoman Zhang, Ya Zhang, Yanfeng Wang, Weidi Xie
• Abstract summary: We consider enhancing medical visual-language pre-training with domain-specific knowledge, by exploiting the paired image-text reports from the radiological daily practice. First, unlike existing works that directly process the raw reports, we adopt a novel triplet extraction module to extract the medical-related information. Second, we propose a novel triplet encoding module with entity translation by querying a knowledge base, to exploit the rich domain knowledge in medical field. Third, we propose to use a Transformer-based fusion model for spatially aligning the entity description with visual signals at the image patch level, enabling the ability for medical diagnosis
• Score: 40.52487429030841
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we consider enhancing medical visual-language pre-training (VLP) with domain-specific knowledge, by exploiting the paired image-text reports from the radiological daily practice. In particular, we make the following contributions: First, unlike existing works that directly process the raw reports, we adopt a novel triplet extraction module to extract the medical-related information, avoiding unnecessary complexity from language grammar and enhancing the supervision signals; Second, we propose a novel triplet encoding module with entity translation by querying a knowledge base, to exploit the rich domain knowledge in medical field, and implicitly build relationships between medical entities in the language embedding space; Third, we propose to use a Transformer-based fusion model for spatially aligning the entity description with visual signals at the image patch level, enabling the ability for medical diagnosis; Fourth, we conduct thorough experiments to validate the effectiveness of our architecture, and benchmark on numerous public benchmarks, e.g., ChestX-ray14, RSNA Pneumonia, SIIM-ACR Pneumothorax, COVIDx CXR-2, COVID Rural, and EdemaSeverity. In both zero-shot and fine-tuning settings, our model has demonstrated strong performance compared with the former methods on disease classification and grounding.

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