Enhancing chest X-ray datasets with privacy-preserving large language models and multi-type annotations: a data-driven approach for improved classification

arXiv:2403.04024v1 Announce Type: cross
Abstract: In chest X-ray (CXR) image analysis, rule-based systems are usually employed to extract labels from reports, but concerns exist about label quality. These datasets typically offer only presence labels, sometimes with binary uncertainty indicators, which limits their usefulness. In this work, we present MAPLEZ (Medical report Annotations with Privacy-preserving Large language model using Expeditious Zero shot answers), a novel approach leveraging a locally executable Large Language Model (LLM) to extract and enhance findings labels on …

abstract analysis annotations arxiv binary classification concerns cs.cv data data-driven datasets eess.iv extract image labels language language models large language large language models privacy quality ray reports systems type uncertainty x-ray