Abstract
Medical reports are an essential medium in recording a patient’s condition throughout a clinical trial. They contain valuable information that can be extracted to generate a large labeled dataset needed for the development of clinical tools. However, the majority of medical reports are stored in an unregularized format, and a trained human annotator (typically a doctor) must manually assess and label each case, resulting in an expensive and time consuming procedure. In this work, we present a framework for developing a multilingual breast MRI report classifier using a custom-built language representation called LAMBR. Our proposed method overcomes practical challenges faced in clinical settings, and we demonstrate improved performance in extracting labels from medical reports when compared with conventional approaches.
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Notes
- 1.
Breast Imaging-Reporting and Data System: a score between 0–6 indicating the level of severity of a breast lesion.
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Tsai, CH., Kiryati, N., Konen, E., Sklair-Levy, M., Mayer, A. (2020). Labeling of Multilingual Breast MRI Reports. In: Cardoso, J., et al. Interpretable and Annotation-Efficient Learning for Medical Image Computing. IMIMIC MIL3ID LABELS 2020 2020 2020. Lecture Notes in Computer Science(), vol 12446. Springer, Cham. https://doi.org/10.1007/978-3-030-61166-8_25
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DOI: https://doi.org/10.1007/978-3-030-61166-8_25
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