CAD-Chest: Comprehensive Annotation of Diseases based on MIMIC-CXR Radiology Report

Several extant chest X-ray (CXR) datasets predominantly comprise binary
disease labels and exhibit a deficiency in providing comprehensive disease-
related information. Crucial facets of disease management, including disease
severity, diagnostic uncertainty, and precise localization, are often absent
in these datasets, yet they hold substantial clinical significance. In this
work, we present a comprehensive annotation of disease (CAD) on CXR images,
which is named CAD-Chest dataset. We have leveraged radiology reports authored
by medical professionals to meticulously devise label extraction protocols.
These protocols facilitate the extraction of essential disease-related
attributes, encompassing disease name, severity grading, and additional
pertinent details. This dataset is poised to empower researchers and
practitioners by offering a holistic perspective on diseases, transcending the
mere presence or absence of binary classification.