/chest_xray_14

Benchmarks on NIH Chest X-ray 14 dataset

[1] [1] [2] [3] [4] [7] [8] [9]
SPLIT BY patient image image patient image image image patient
OFFICIAL SPLIT Yes No No No No No No Yes
Atelectasis 0.7003 0.72 0.81 0.772 0.80 0.76 0.853 0.767
Cardiomegaly 0.8100 0.81 0.904 0.9248 0.81 0.91 0.939 0.883
Effusion 0.7585 0.78 0.859 0.8638 0.87 0.86 0.903 0.828
Infiltration 0.6614 0.61 0.695 0.7345 0.70 0.69 0.754 0.709
Mass 0.6933 0.71 0.792 0.8676 0.83 0.78 0.902 0.821
Nodule 0.6687 0.67 0.717 0.7802 0.75 0.70 0.828 0.758
Pneumonia 0.6580 0.63 0.713 0.7680 0.67 0.71 0.774 0.731
Pneumothorax 0.7993 0.81 0.841 0.8887 0.87 0.86 0.921 0.846
Consolidation 0.7032 0.71 0.788 0.7901 0.80 0.78 0.842 0.745
Edema 0.8052 0.83 0.882 0.8878 0.88 0.89 0.924 0.835
Emphysema 0.8330 0.81 0.829 0.9371 0.91 0.90 0.932 0.895
Fibrosis 0.7859 0.77 0.767 0.8047 0.78 0.76 0.864 0.818
Pleural Thickening 0.6835 0.71 0.765 0.8062 0.79 0.77 0.837 0.761
Hernia 0.8717 0.77 0.914 0.9164 0.77 0.90 0.921 0.896

Split by image: This repo contains the splits of train, valid and test.

Split by patient: Please be aware that the official splits by patient are only recently available here

Please contribute to the following list:

[1] ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases

[2] Learning to diagnose from scratch by exploiting dependencies among labels

[3] CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning

[4] Thoracic Disease Identification and Localization with Limited Supervision

[5] Learning to detect chest radiographs containing lung nodules using visual attention networks(Private dataset)

[6] TieNet: Text-Image Embedding Network for Common Thorax Disease Classification and Reporting in Chest X-rays (different tasks, no improvement on using only images)

[7] Boosted Cascaded Convnets for Multilabel Classification of Thoracic Diseases in Chest Radiographs

[8] Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification

[9] Learning to recognize Abnormalities in Chest X-Rays with Location-Aware Dense Networks (using extra PLCO dataset)