Learning to Summarize Radiology Findings

By Yuhao Zhang, Daisy Yi Ding, Tianpei Qian, Christopher D. Manning, Curtis P. Langlotz. 2018

139 citations recorded by the source corpus.

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 human-written summaries, suggesting significant clinical validity. To our knowledge our work represents the first attempt in this direction.

Subfields: Artificial Intelligence, Molecular Biology

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Citations in the corpus

Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports (2019, methodology): “To study whether the improvements in the factual correctness scores lead to improvement in summarization quality under expert judgment, we run a comparative human evaluation following previous work (Chen and Bansal, 2018; Dong et al., 2018; Zhang et al., 2018).”
ChatGPT makes medicine easy to swallow: an exploratory case study on simplified radiology reports (2022, background): “A supervised recurrent model for this domain was proposed by [23] with increased performance in comparison to previous non-neural approaches.”
Making the Most of Text Semantics to Improve Biomedical Vision-Language Processing (2022, background): “[83] Yuhao Zhang, Daisy Yi Ding, Tianpei Qian, Christopher D Manning, and Curtis P Langlotz.”
Human Evaluation and Correlation with Automatic Metrics in Consultation Note Generation (2022, unknown): “, 2016b; Alsentzer and Kim, 2018), and with abstractive neural methods (Zhang et al., 2018; Liu et al., 2019; MacAvaney et al., 2019; Zhang et al., 2020; Enarvi et al., 2020; Joshi et al., 2020; Krishna et al., 2021; Chintagunta et al., 2021; Yim and Yetisgen-Yildiz, 2021; Moramarco et al., 2021; Zh”
Cross-modal Memory Networks for Radiology Report Generation (2022, methodology): “Recently, many advanced NLP techniques (e.g., pre-trained language models) have been applied to tasks in the medical domain (Pampari et al., 2018; Zhang et al., 2018; Wang et al., 2018; Alsentzer et al., 2019; Tian et al., 2019, 2020a; Wang et al., 2020; Lee et al., 2020; Song et al., 2020).”