Factual Serialization Enhancement: A Key Innovation for Chest X-ray Report Generation
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866914607604432896 |
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| author | Liu, Kang Ma, Zhuoqi Liu, Mengmeng Jiao, Zhicheng Kang, Xiaolu Miao, Qiguang Xie, Kun |
| author_facet | Liu, Kang Ma, Zhuoqi Liu, Mengmeng Jiao, Zhicheng Kang, Xiaolu Miao, Qiguang Xie, Kun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_09586 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Factual Serialization Enhancement: A Key Innovation for Chest X-ray Report Generation Liu, Kang Ma, Zhuoqi Liu, Mengmeng Jiao, Zhicheng Kang, Xiaolu Miao, Qiguang Xie, Kun Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition 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. |
| title | Factual Serialization Enhancement: A Key Innovation for Chest X-ray Report Generation |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.09586 |