  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.
