Chest X-ray Dataset

Dataset

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MIMIC-CXR

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377,110 images, 227,835 reports

NIH-CXR

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112,120 X-ray images from 30,805 unique patients, with labels of 14 diseases

The labels are expected to be >90% accurate and suitable for weakly-supervised learning

CheXpert

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224,316 images of 65,240 patients, labels of 14 diseases: positive, negative, uncertain

Train: all labeled by CheXpert Labeler

Validation: 200, labeled by experts

Test: 500, labeled by experts

PadChest

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• 160,000+ images
  • from 67,000 patients, from 2009 to 2017
• reports
  • labeled with 174 different radiographic findings, 19 differential diagnoses and 104 anatomic locations
  • 27% were manually annotated and the remaining set was labeled by RNN (0.93 Micro-F1 score)

RadGraph

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structure clinical information in a radiology report with 4 entities and 3 relations, with MIMIC-CXR and CheXpert

https://physionet.org/content/radgraph/1.0.0/

• Annotations: 500 for train, 100 for test
  • train.json: File containing annotations obtained by board-certified radiologists for 425 radiology reports (MIMIC-CXR)
  • dev.json: File containing annotations obtained by board-certified radiologists for 75 radiology reports (MIMIC-CXR)
  • test.json: File containing annotations obtained by 2 different board-certified radiologists for 100 radiology reports (50 MIMIC-CXR, 50 CheXpert)
• Graphs
  • MIMIC-CXR_graphs.json: File containing annotations obtained by our deep learning model for 220,763 radiology reports (MIMIC-CXR)
  • CheXpert_graphs.json: File containing annotations obtained by our deep learning model for 500 radiology reports (CheXpert)
• Models
  • models/model_checkpoint: Folder containing the saved model parameters used to automatically generate the annotations in MIMIC-CXR_graphs.json and CheXpert_graphs.json
  • models/README.txt: File containing instructions for performing inference using model checkpoint
  • models/inference.py: File containing code to extract entities and relations given a directory containing reports in txt format

Chest ImaGenome

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• scene graph to describe 242,072 images
• bounding boxes for 29 unique anatomical regions in the chest
• sentences describing each region if they exist in the corresponding radiology report

Chest Disease

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