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Learning to Summarize Radiology Findings
Open accessAbstract
The Impression section of a radiology report summarizes crucial radiology findings in natural language and plays a central role in communicating these findings to physicians.However, the process of generating impressions by summarizing findings is time-consuming for radiologists and prone to errors.We propose to automate the generation of radiology impressions with neural sequence-to-sequence learning.We further propose a customized neural model for this task which learns to encode the study background information and use this information to guide the decoding process.On a large dataset of radiology reports collected from actual hospital studies, our model outperforms existing non-neural and neural baselines under the ROUGE metrics.In a blind experiment, a board-certified radiologist indicated that 67% of sampled system summaries are at least as good as the corresponding humanwritten summaries, suggesting significant clinical validity.To our knowledge our work represents the first attempt in this direction.
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Singh et al. (2019) used GloVe and RadGloVe word-embedding databases to train the LSTM and used BLEU, METEOR, CIDER, and ROUGE metrics.
“They used word-embedding databases Global Vectors (GloVe) (Pennington et al. (2014)) and the radiology variant RadGloVe (Zhang et al. (2018)) to train the LSTM, and used the aforementioned BLEU metric as well as variants METEOR, CIDER, and ROUGE (Banerjee and Lavie (2005); Lin (2004); Vedantam et al. (2015)).”
From Explainable artificial intelligence (XAI) in deep learning-based medical image analysis (2022) · cites this paper as Zhang et al. (2018)
Most authors focused on summarization of radiology reports, i.e., on the creation of a shorter version that includes all important aspects at the same level of text complexity.
“So far, most authors focused on summarization of radiology reports, i.e., on the creation of a shorter version that includes all important aspects at the same level of text complexity [23–26].”
From ChatGPT makes medicine easy to swallow: an exploratory case study on simplified radiology reports (2023) · cites this paper as [23]
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Metadata
- Authors
- Zhang, Y., Ding, D. Y., Qian, T., Manning, C. D., & Langlotz, C. P.
- Affiliations
- Stanford University (US)
- Venue
- Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis
- Published
- 2018
- Pages
- 204-213
- OpenAlex
- W2891022667
- Cited by
- 126
- References
- 26
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 22 · 2023: 31 · 2022: 16 · 2021: 24 · 2020: 12 · 2019: 9 · 2018: 2
- FWCI
- 3.54
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- International Workshop on Health Text Mining and Information Analysis (LOUHI) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- radiology report summarization, sequence-to-sequence learning, clinical text summarization, ROUGE evaluation
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