  Chest radiography is an extremely powerful imaging modality, allowing for a
detailed inspection of a patient's thorax, but requiring specialized training
for proper interpretation. With the advent of high performance general purpose
computer vision algorithms, the accurate automated analysis of chest
radiographs is becoming increasingly of interest to researchers. However, a key
challenge in the development of these techniques is the lack of sufficient
data. Here we describe MIMIC-CXR-JPG v2.0.0, a large dataset of 377,110 chest
x-rays associated with 227,827 imaging studies sourced from the Beth Israel
Deaconess Medical Center between 2011 - 2016. Images are provided with 14
labels derived from two natural language processing tools applied to the
corresponding free-text radiology reports. MIMIC-CXR-JPG is derived entirely
from the MIMIC-CXR database, and aims to provide a convenient processed version
of MIMIC-CXR, as well as to provide a standard reference for data splits and
image labels. All images have been de-identified to protect patient privacy.
The dataset is made freely available to facilitate and encourage a wide range
of research in medical computer vision.
