- 【Updated on May 12, 2025】 Integration of CiNii Dissertations and CiNii Books into CiNii Research
- Trial version of CiNii Research Knowledge Graph Search feature is available on CiNii Labs
- 【Updated on June 30, 2025】Suspension and deletion of data provided by Nikkei BP
- Regarding the recording of “Research Data” and “Evidence Data”
-
- Zhang, Mengliang
- Creator
-
- Hu, Xinyue
- Creator
-
- Gu, Lin
- Creator
-
- Harada, Tatsuya
- Creator
-
- Kobayashi, Kazuma
- Creator
-
- Summers, Ronald
- Creator
-
- Zhu, Yingying
- Creator
Metadata
- Published
- 2023-01-01
- DOI
-
- 10.13026/44pd-vz36
- 10.13026/kyfs-5608
- Publisher
- PhysioNet
- Creator Name (e-Rad)
-
- Zhang, Mengliang
- Hu, Xinyue
- Gu, Lin
- Harada, Tatsuya
- Kobayashi, Kazuma
- Summers, Ronald
- Zhu, Yingying
Description
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.