LEAVS: An LLM-based Labeler for Abdominal CT Supervision
Journal:
arXiv
Published Date:
Mar 17, 2025
Abstract
Extracting structured labels from radiology reports has been employed to
create vision models to simultaneously detect several types of abnormalities.
However, existing works focus mainly on the chest region. Few works have been
investigated on abdominal radiology reports due to more complex anatomy and a
wider range of pathologies in the abdomen. We propose LEAVS (Large language
model Extractor for Abdominal Vision Supervision). This labeler can annotate
the certainty of presence and the urgency of seven types of abnormalities for
nine abdominal organs on CT radiology reports. To ensure broad coverage, we
chose abnormalities that encompass most of the finding types from CT reports.
Our approach employs a specialized chain-of-thought prompting strategy for a
locally-run LLM using sentence extraction and multiple-choice questions in a
tree-based decision system. We demonstrate that the LLM can extract several
abnormality types across abdominal organs with an average F1 score of 0.89,
significantly outperforming competing labelers and humans. Additionally, we
show that extraction of urgency labels achieved performance comparable to human
annotations. Finally, we demonstrate that the abnormality labels contain
valuable information for training a single vision model that classifies several
organs as normal or abnormal. We release our code and structured annotations
for a public CT dataset containing over 1,000 CT volumes.
