EVOKE: Elevating Chest X-ray Report Generation via Multi-View Contrastive Learning and Patient-Specific Knowledge

• URL: http://arxiv.org/abs/2411.10224v2
• Date: Wed, 12 Mar 2025 09:38:02 GMT
• Title: EVOKE: Elevating Chest X-ray Report Generation via Multi-View Contrastive Learning and Patient-Specific Knowledge
• Authors: Qiguang Miao, Kang Liu, Zhuoqi Ma, Yunan Li, Xiaolu Kang, Ruixuan Liu, Tianyi Liu, Kun Xie, Zhicheng Jiao,
• Abstract summary: textbfEVOKE is a novel chest X-ray report generation framework that incorporates multi-view contrastive learning and patient-specific knowledge.<n>We present a knowledge-guided report generation module that integrates available patient-specific indications.<n>Our proposed EVOKE surpasses recent state-of-the-art methods across multiple datasets.
• Score: 21.596462896333733
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Radiology reports are crucial for planning treatment strategies and facilitating effective doctor-patient communication. However, the manual creation of these reports places a significant burden on radiologists. While automatic radiology report generation presents a promising solution, existing methods often rely on single-view radiographs, which constrain diagnostic accuracy. To address this challenge, we propose \textbf{EVOKE}, a novel chest X-ray report generation framework that incorporates multi-view contrastive learning and patient-specific knowledge. Specifically, we introduce a multi-view contrastive learning method that enhances visual representation by aligning multi-view radiographs with their corresponding report. After that, we present a knowledge-guided report generation module that integrates available patient-specific indications (e.g., symptom descriptions) to trigger the production of accurate and coherent radiology reports. To support research in multi-view report generation, we construct Multi-view CXR and Two-view CXR datasets using publicly available sources. Our proposed EVOKE surpasses recent state-of-the-art methods across multiple datasets, achieving a 2.9\% F\textsubscript{1} RadGraph improvement on MIMIC-CXR, a 7.3\% BLEU-1 improvement on MIMIC-ABN, a 3.1\% BLEU-4 improvement on Multi-view CXR, and an 8.2\% F\textsubscript{1,mic-14} CheXbert improvement on Two-view CXR.

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