Factual Serialization Enhancement: A Key Innovation for Chest X-ray Report Generation

• URL: http://arxiv.org/abs/2405.09586v2
• Date: Thu, 12 Sep 2024 03:11:41 GMT
• Title: Factual Serialization Enhancement: A Key Innovation for Chest X-ray Report Generation
• Authors: Kang Liu, Zhuoqi Ma, Mengmeng Liu, Zhicheng Jiao, Xiaolu Kang, Qiguang Miao, Kun Xie,
• Abstract summary: A radiology report comprises presentation-style vocabulary, which ensures clarity and organization. A critical step in this process is to align radiographs with their corresponding reports. To address this issue, we propose FSE, a two-stage Factual Serialization Enhancement method.
• Score: 15.82363717056198
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A radiology report comprises presentation-style vocabulary, which ensures clarity and organization, and factual vocabulary, which provides accurate and objective descriptions based on observable findings. While manually writing these reports is time-consuming and labor-intensive, automatic report generation offers a promising alternative. A critical step in this process is to align radiographs with their corresponding reports. However, existing methods often rely on complete reports for alignment, overlooking the impact of presentation-style vocabulary. To address this issue, we propose FSE, a two-stage Factual Serialization Enhancement method. In Stage 1, we introduce factuality-guided contrastive learning for visual representation by maximizing the semantic correspondence between radiographs and corresponding factual descriptions. In Stage 2, we present evidence-driven report generation that enhances diagnostic accuracy by integrating insights from similar historical cases structured as factual serialization. Experiments on MIMIC-CXR and IU X-ray datasets across specific and general scenarios demonstrate that FSE outperforms state-of-the-art approaches in both natural language generation and clinical efficacy metrics. Ablation studies further emphasize the positive effects of factual serialization in Stage 1 and Stage 2. The code is available at https://github.com/mk-runner/FSE.

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