Labeling of Multilingual Breast MRI Reports
This work addresses the need for efficient labeling of medical reports to support clinical tool development, though it appears incremental as it builds on existing methods for a specific domain.
The authors tackled the problem of expensive and time-consuming manual labeling of multilingual breast MRI reports by developing a framework using a custom language representation called LAMBR, which demonstrated improved performance in extracting labels compared to conventional approaches.
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.