CXR-Align: A Benchmark for CXR-Report Alignment with Negations

CXR-Align is a benchmark dataset designed to evaluate vision-language
processing (VLP) models' ability to accurately interpret negations in chest
X-ray (CXR) reports. Negations are prevalent in medical documentation and pose
significant challenges for automated analysis, as misinterpretation can lead
to critical diagnostic errors. Existing medical VLP systems often inadequately
handle negated findings, motivating the creation of CXR-Align to specifically
target and mitigate this limitation.

The dataset comprises systematically modified and anonymized clinical reports
derived from the MIMIC-III database. Each report in CXR-Align includes
controlled alterations, particularly negations introduced to clinically
relevant positive findings. Reports are standardized to exclude normal CXR
cases, ensuring that each entry maintains direct diagnostic significance and
maximizes the datase's utility for assessing nuanced language comprehension.

CXR-Align enables researchers to rigorously test the accuracy and robustness
of VLP models, advancing the development of systems capable of reliably
interpreting complex clinical language. The dataset's structured format and
clear diagnostic focus make it an essential resource for researchers aiming to
enhance model comprehension of medical negations, ultimately contributing to
safer and more effective clinical decision-support tools.