Labeling of Multilingual Breast MRI Reports

TL;DR

This paper introduces LAMBR, a custom language model for automatically classifying multilingual breast MRI reports, reducing the need for manual labeling and improving extraction accuracy in clinical settings.

Contribution

The paper presents a novel multilingual report classifier using LAMBR, addressing practical challenges and outperforming conventional methods in medical report labeling.

Findings

LAMBR improves label extraction accuracy

Reduces manual annotation effort

Effective across multiple languages

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.