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

メタデータ

公開日
2023-01-01
DOI
  • 10.13026/44pd-vz36
  • 10.13026/kyfs-5608
公開者
PhysioNet
データ作成者 (e-Rad)
  • Zhang, Mengliang
  • Hu, Xinyue
  • Gu, Lin
  • Harada, Tatsuya
  • Kobayashi, Kazuma
  • Summers, Ronald
  • Zhu, Yingying

説明

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.

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