MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs

TL;DR

MIMIC-CXR-JPG v2.0.0 is a large, publicly available dataset of over 377,000 labeled chest X-ray images with associated reports, designed to support research in automated medical image analysis.

Contribution

This paper introduces a large, de-identified chest X-ray dataset with standardized labels and data splits to advance medical computer vision research.

Findings

Provides a comprehensive dataset for training deep learning models.

Includes standardized labels derived from radiology reports.

Facilitates reproducibility and benchmarking in medical image analysis.

Abstract

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.