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

By Alistair E. W. Johnson, Tom J. Pollard, N. Greenbaum, Matthew P. Lungren, Chih-Ying Deng, Yifan Peng, Zhiyong Lu, R. Mark, Seth J. Berkowitz, Steven Horng. 2019

1,117 citations recorded by the source corpus.

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.

Subfields: Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/3d29ce781f297dc543e44dfb39990baff3a3acca

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In Pulmonary and Respiratory Medicine

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

GLoRIA: A Multimodal Global-Local Representation Learning Framework for Label-efficient Medical Image Recognition (2021, methodology): “To leverage information from radiology reports, a number of previous work explore methods for extracting labels from reports via natural language processing (NLP) as a surrogate for manual annotations [16, 35, 18].”
CheXclusion: Fairness gaps in deep chest X-ray classifiers (2020, methodology): “(‘Airspace Opacity’ in [22] and ‘Lung Opacity’ in [21] denote a same label.”
Self-supervised learning for medical image classification: a systematic review and implementation guidelines (2023, background): “The choice of this domain is likely a combination of the availability of large-scale public chest datasets such as CheXpert 73 , RSPECT 74 , RadFusion 75 and MIMIC-CXR 76 , as well as the motivation to solve acute or emerging healthcare threats, which was the case during the coronavirus pandemic 45,”
Cross-modal Memory Networks for Radiology Report Generation (2022, methodology): “, 2016)6 from Indiana University and MIMIC-CXR (Johnson et al., 2019)7 from the Beth Israel Deaconess Medical Center.”
Opportunities and Challenges for ChatGPT and Large Language Models in Biomedicine and Health (2023, methodology): “Representative datasets include MIMIC-CXR [81], which is a large-scale radiography dataset comprising 473,057 chest X-ray images and 206,563 reports.”
An adversarial approach for the robust classification of pneumonia from chest radiographs (2020, methodology): “For our target domain, we used the MIMIC-CXR dataset from Massachusetts Institute of Technology [15].”
COVID-19 Image Data Collection: Prospective Predictions Are the Future (2020, background): “, 2019] or MIMIC-CXR [Johnson et al., 2019]), neural networks have even been able to achieve performance near radiologist levels [Rajpurkar et al.”
MoCo Pretraining Improves Representation and Transferability of Chest X-ray Models (2020, background): “The recent introduction of large datasets (size 100k-500k) of chest X-rays (Irvin et al., 2019; Johnson et al., 2019; Bustos et al., 2020) has driven the development of deep learning models that can detect diseases at a level comparable to radiologists (Rajpurkar et al., 2020).”
Natural Language Processing in Electronic Health Records in relation to healthcare decision-making: A systematic review (2023, background): “It achieved a new state-of-the-art result by improving F1 scores for a report labelling task on the MIMIC-CXR dataset [60], which contains large-scale labelled chest radiographs.”
Can we trust deep learning models diagnosis? The impact of domain shift in chest radiograph classification (2019, background): “Among the analyzed datasets, CheXpert and MIMIC-CXR seem to be most representative of the other two, as the models trained on them show a smaller performance drop when comparing to the baseline.”
Transformers in Medical Imaging: A Survey (2022, methodology): “Image ReportDataset Number Modality View* Length* Language Cases IU X-ray [341] 7,470 X-Ray 2 32.5 Eng. 2,955 MIMIC-CXR [352] 377,110 X-Ray 1 53.2 Eng. 276,778 PadChest [355] 160,868 X-Ray 2 - Es 22,710 CX-CHR [356] 45,598 X-Ray 2 66.9 Zh 40,410 DIARETDB1 [357] 89 CFP 1 - Eng 89 MESSIDOR [261] 1,200”
Integrated multimodal artificial intelligence framework for healthcare applications (2022, methodology): “0.0 37 containing 377,110 radiology images with free-text reports representing 227,835 medical imaging events that can be matched to corresponding patients included in MIMIC-IV v1.”
Multimodal Representation Learning via Maximization of Local Mutual Information (2021, methodology): “Detecting 9 pathologies from the chest radiographs against the labels that were extracted from the corresponding radiology reports using a radiology report labeler CheXpert [12,14,15].”
CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT (2020, methodology): “The CheXpert labeler has been applied to generate labels for the CheXpert dataset and MIMIC-CXR (Johnson et al., 2019), which are amongst the largest chest x-ray datasets publicly available.”
Exploring and Distilling Posterior and Prior Knowledge for Radiology Report Generation (2021, methodology): “As shown in Table 1, our PPKED outperforms state-of-the-art methods across all metrics on both MIMIC-CXR and IU-Xray datasets.”
Generating Radiology Reports via Memory-driven Transformer (2020, background): “6Note that CE metrics only apply to MIMIC-CXR because the labeling schema of CheXpert is designed for MIMIC-CXR, which is different from that of IU X-RAY.”