Automated Generation of Accurate \& Fluent Medical X-ray Reports

• URL: http://arxiv.org/abs/2108.12126v1
• Date: Fri, 27 Aug 2021 05:47:28 GMT
• Title: Automated Generation of Accurate \& Fluent Medical X-ray Reports
• Authors: Hoang T.N. Nguyen, Dong Nie, Taivanbat Badamdorj, Yujie Liu, Yingying Zhu, Jason Truong, Li Cheng
• Abstract summary: The paper focuses on automating the generation of medical reports from chest X-ray image inputs. Our approach achieved promising results on commonly-used metrics concerning language fluency and clinical accuracy.
• Score: 17.927768992248172
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Our paper focuses on automating the generation of medical reports from chest X-ray image inputs, a critical yet time-consuming task for radiologists. Unlike existing medical re-port generation efforts that tend to produce human-readable reports, we aim to generate medical reports that are both fluent and clinically accurate. This is achieved by our fully differentiable and end-to-end paradigm containing three complementary modules: taking the chest X-ray images and clinical his-tory document of patients as inputs, our classification module produces an internal check-list of disease-related topics, referred to as enriched disease embedding; the embedding representation is then passed to our transformer-based generator, giving rise to the medical reports; meanwhile, our generator also pro-duces the weighted embedding representation, which is fed to our interpreter to ensure consistency with respect to disease-related topics.Our approach achieved promising results on commonly-used metrics concerning language fluency and clinical accuracy. Moreover, noticeable performance gains are consistently ob-served when additional input information is available, such as the clinical document and extra scans of different views.

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