Examining the Efficacy of Transformer Models in Radiology Report Labeling within a Thai Hospital
Conference proceedings article
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Publication Details
Author list: Larpkiattaworn W., Promwisat T., Chamveha I., Chaisangmongkon W.
Publisher: Institute of Electrical and Electronics Engineers Inc.
Publication year: 2024
Start page: 226
End page: 231
Number of pages: 6
ISBN: 9798350344349
Languages: English-Great Britain (EN-GB)
Abstract
This study explores the application of Transformer models, specifically CheXbert and CharacterBERT, in extracting labels from radiology reports in a real-world clinical setting of a Thai hospital. This setting presents unique challenges, such as spelling errors, grammar mistakes, and diverse report formats, leading to 'noisy labels'. Previous natural language processing systems, including rule-based algorithms and Transformers, have been used for this task, but they face difficulties in such environment. Despite these challenges, our research demonstrates that training Transformers on a small dataset is sufficient to outperform rule-based labelers. The study also reveals that increasing dataset size and data augmentation do not necessarily enhance accuracy, due to the potential increase in noise. Further, a comparison between CharacterBERT and CheXbert is made, showing that despite CharacterBERT's ability to handle misspellings, its accuracy does not consistently surpass that of CheXbert. The paper concludes with a case study demonstrating how CheXbert, in collaboration with rule-based labelers, can assist in identifying and rectifying potentially noisy reports, thereby aiding in label purification. © 2024 IEEE.
Keywords
chest X-ray report labeling
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