Toward expanding the scope of radiology report summarization to multiple anatomies and modalities
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866909120603357184 |
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| author | Chen, Zhihong Varma, Maya Wan, Xiang Langlotz, Curtis Delbrouck, Jean-Benoit |
| author_facet | Chen, Zhihong Varma, Maya Wan, Xiang Langlotz, Curtis Delbrouck, Jean-Benoit |
| contents | Radiology report summarization (RRS) is a growing area of research. Given the Findings section of a radiology report, the goal is to generate a summary (called an Impression section) that highlights the key observations and conclusions of the radiology study. However, RRS currently faces essential limitations.First, many prior studies conduct experiments on private datasets, preventing reproduction of results and fair comparisons across different systems and solutions. Second, most prior approaches are evaluated solely on chest X-rays. To address these limitations, we propose a dataset (MIMIC-RRS) involving three new modalities and seven new anatomies based on the MIMIC-III and MIMIC-CXR datasets. We then conduct extensive experiments to evaluate the performance of models both within and across modality-anatomy pairs in MIMIC-RRS. In addition, we evaluate their clinical efficacy via RadGraph, a factual correctness metric. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_08584 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Toward expanding the scope of radiology report summarization to multiple anatomies and modalities Chen, Zhihong Varma, Maya Wan, Xiang Langlotz, Curtis Delbrouck, Jean-Benoit Computation and Language Machine Learning Radiology report summarization (RRS) is a growing area of research. Given the Findings section of a radiology report, the goal is to generate a summary (called an Impression section) that highlights the key observations and conclusions of the radiology study. However, RRS currently faces essential limitations.First, many prior studies conduct experiments on private datasets, preventing reproduction of results and fair comparisons across different systems and solutions. Second, most prior approaches are evaluated solely on chest X-rays. To address these limitations, we propose a dataset (MIMIC-RRS) involving three new modalities and seven new anatomies based on the MIMIC-III and MIMIC-CXR datasets. We then conduct extensive experiments to evaluate the performance of models both within and across modality-anatomy pairs in MIMIC-RRS. In addition, we evaluate their clinical efficacy via RadGraph, a factual correctness metric. |
| title | Toward expanding the scope of radiology report summarization to multiple anatomies and modalities |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2211.08584 |