CLARA: Clinical Report Auto-completion
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Abstract (arXiv)
Generating clinical reports from raw recordings such as X-rays and electroencephalogram (EEG) is an essential and routine task for doctors. However, it is often time-consuming to write accurate and detailed reports. Most existing methods try to generate the whole reports from the raw input with limited success because 1) generated reports often contain errors that need manual review and correction, 2) it does not save time when doctors want to write additional information into the report, and 3) the generated reports are not customized based on individual doctors' preference. We propose it CLinicit Al it Report it Auto-completion (CLARA), an interactive method that generates reports in a sentence by sentence fashion based on doctors' anchor words and partially completed sentences. CLARA searches for most relevant sentences from existing reports as the template for the current report. The retrieved sentences are sequentially modified by combining with the input feature representations to create the final report. In our experimental evaluation, CLARA achieved 0.393 CIDEr and 0.248 BLEU-4 on X-ray reports and 0.482 CIDEr and 0.491 BLEU-4 for EEG reports for sentence-level generation, which is up to 35% improvement over the best baseline. Also via our qualitative evaluation, CLARA is shown to produce reports which have a significantly higher level of approval by doctors in a user study (3.74 out of 5 for CLARA vs 2.52 out of 5 for the baseline).
Key facts. Cited by 5 papers in the map (data through 13 Oct 2026). First posted on arXiv 26 Feb 2020.
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Citing papers per year: 2021: 1; 2022: 3; 2023: 0; 2024: 1.
5 papers.
- 2022AlignTransformer: Hierarchical Alignment of Visual Regions and Disease Tags for Medical Report Generationcited by 65
- 2021Writing by Memorizing: Hierarchical Retrieval-based Medical Report Generationcited by 19
- 2022Attributed Abnormality Graph Embedding for Clinically Accurate X-Ray Report Generationcited by 17
- 2022Democratizing Ethical Assessment of Natural Language Generation Modelscited by 0
- 2024Advancing Histopathology-Based Breast Cancer Diagnosis: Insights into Multi-Modality and Explainabilitycited by 0
- 2024Advancing Histopathology-Based Breast Cancer Diagnosis: Insights into Multi-Modality and Explainability7 Jun 2024
- 2022Attributed Abnormality Graph Embedding for Clinically Accurate X-Ray Report Generation4 Jul 2022
- 2022Democratizing Ethical Assessment of Natural Language Generation Models30 Jun 2022
- 2022AlignTransformer: Hierarchical Alignment of Visual Regions and Disease Tags for Medical Report Generation18 Mar 2022
- 2021Writing by Memorizing: Hierarchical Retrieval-based Medical Report Generation25 May 2021