Automated Labeling of German Chest X-Ray Radiology Reports using Deep Learning
This work addresses the shortage of radiologists by enabling faster dataset creation for clinical decision-support systems, though it is incremental as it builds on existing methods like CheXpert.
The paper tackled the problem of automating label extraction from German chest X-ray radiology reports to reduce manual labeling costs for deep learning models, achieving significant performance improvements over a rule-based baseline across all tasks.
Radiologists are in short supply globally, and deep learning models offer a promising solution to address this shortage as part of clinical decision-support systems. However, training such models often requires expensive and time-consuming manual labeling of large datasets. Automatic label extraction from radiology reports can reduce the time required to obtain labeled datasets, but this task is challenging due to semantically similar words and missing annotated data. In this work, we explore the potential of weak supervision of a deep learning-based label prediction model, using a rule-based labeler. We propose a deep learning-based CheXpert label prediction model, pre-trained on reports labeled by a rule-based German CheXpert model and fine-tuned on a small dataset of manually labeled reports. Our results demonstrate the effectiveness of our approach, which significantly outperformed the rule-based model on all three tasks. Our findings highlight the benefits of employing deep learning-based models even in scenarios with sparse data and the use of the rule-based labeler as a tool for weak supervision.