Most of the existing chest X-ray datasets include labels from a list of findings without specifying their locations on the radiographs. This limits the development of machine learning algorithms for the detection and localization of chest abnormalities. In this work, we describe a dataset of more than 100,000 chest X-ray scans that were retrospectively collected from two major hospitals in Vietnam. Out of this raw data, we release 18,000 images that were manually annotated by a total of 17 experienced radiologists with 22 local labels of rectangles surrounding abnormalities and 6 global labels of suspected diseases. The released dataset is divided into a training set of 15,000 and a test set of 3,000. Each scan in the training set was independently labeled by 3 radiologists, while each scan in the test set was labeled by the consensus of 5 radiologists. We designed and built a labeling platform for DICOM images to facilitate these annotation procedures. All images are made publicly available (https://www.physionet.org/content/vindr-cxr/1.0.0/) in DICOM format along with the labels of both the training set and the test set.
@article{arxiv.2012.15029,
title = {VinDr-CXR: An open dataset of chest X-rays with radiologist's annotations},
author = {Ha Q. Nguyen and Khanh Lam and Linh T. Le and Hieu H. Pham and Dat Q. Tran and Dung B. Nguyen and Dung D. Le and Chi M. Pham and Hang T. T. Tong and Diep H. Dinh and Cuong D. Do and Luu T. Doan and Cuong N. Nguyen and Binh T. Nguyen and Que V. Nguyen and Au D. Hoang and Hien N. Phan and Anh T. Nguyen and Phuong H. Ho and Dat T. Ngo and Nghia T. Nguyen and Nhan T. Nguyen and Minh Dao and Van Vu},
journal= {arXiv preprint arXiv:2012.15029},
year = {2022}
}