|
[1] |
[1] |
[2] |
[3] |
[4] |
[7] |
[8] |
[9] |
[10] |
| SPLIT BY |
patient |
image |
image |
patient |
image |
image |
image |
patient |
patient |
| OFFICIAL SPLIT |
Yes |
No |
No |
No |
No |
No |
No |
Yes |
Yes |
| Atelectasis |
0.7003 |
0.72 |
0.81 |
0.772 |
0.80 |
0.76 |
0.853 |
0.767 |
0.733 |
| Cardiomegaly |
0.8100 |
0.81 |
0.904 |
0.9248 |
0.81 |
0.91 |
0.939 |
0.883 |
0.858 |
| Effusion |
0.7585 |
0.78 |
0.859 |
0.8638 |
0.87 |
0.86 |
0.903 |
0.828 |
0.806 |
| Infiltration |
0.6614 |
0.61 |
0.695 |
0.7345 |
0.70 |
0.69 |
0.754 |
0.709 |
0.675 |
| Mass |
0.6933 |
0.71 |
0.792 |
0.8676 |
0.83 |
0.78 |
0.902 |
0.821 |
0.727 |
| Nodule |
0.6687 |
0.67 |
0.717 |
0.7802 |
0.75 |
0.70 |
0.828 |
0.758 |
0.778 |
| Pneumonia |
0.6580 |
0.63 |
0.713 |
0.7680 |
0.67 |
0.71 |
0.774 |
0.731 |
0.690 |
| Pneumothorax |
0.7993 |
0.81 |
0.841 |
0.8887 |
0.87 |
0.86 |
0.921 |
0.846 |
0.805 |
| Consolidation |
0.7032 |
0.71 |
0.788 |
0.7901 |
0.80 |
0.78 |
0.842 |
0.745 |
0.717 |
| Edema |
0.8052 |
0.83 |
0.882 |
0.8878 |
0.88 |
0.89 |
0.924 |
0.835 |
0.806 |
| Emphysema |
0.8330 |
0.81 |
0.829 |
0.9371 |
0.91 |
0.90 |
0.932 |
0.895 |
0.842 |
| Fibrosis |
0.7859 |
0.77 |
0.767 |
0.8047 |
0.78 |
0.76 |
0.864 |
0.818 |
0.757 |
| Pleural Thickening |
0.6835 |
0.71 |
0.765 |
0.8062 |
0.79 |
0.77 |
0.837 |
0.761 |
0.724 |
| Hernia |
0.8717 |
0.77 |
0.914 |
0.9164 |
0.77 |
0.90 |
0.921 |
0.896 |
0.824 |
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 (SOTA achived with extra PLCO dataset)
[10] Weakly Supervised Medical Diagnosis and Localization from Multiple Resolutions (SOTA without using extra data)
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