Datasets:
metadata
license: cc0-1.0
task_categories:
- image-classification
tags:
- medical
- chest-x-ray
- multi-label
- radiology
pretty_name: NIH ChestX-ray14 (Preprocessed)
size_categories:
- 100K<n<1M
NIH ChestX-ray14 — Preprocessed Dataset
Dataset Description
Preprocessed version of the NIH ChestX-ray14 dataset for multi-label thoracic disease classification.
Source
- Original Dataset: NIH ChestX-ray14
- Institution: NIH Clinical Center
- License: CC0 1.0 (Public Domain)
Citation
@inproceedings{wang2017chestx,
title={ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases},
author={Wang, Xiaosong and Peng, Yifan and Lu, Le and Lu, Zhiyong and Bagheri, Mohammadhadi and Summers, Ronald M},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
pages={2097--2106},
year={2017}
}
Preprocessing Pipeline
1. Quality Filtering
Images were filtered based on pixel intensity statistics from the original 112,120 images:
- Too dark (mean < 50): 61 images removed, 28 manually cropped (black border removal) and retained
- Too bright (mean > 195): 52 images removed, 2 manually cropped (white border removal) and retained
- Low contrast (std < 25 after CLAHE): 28 images removed, 1 retained (00004480_000.png)
- Total removed: 141 images
- Total retained: 111,979 images
2. Image Processing (applied to all retained images)
- CLAHE (Contrast Limited Adaptive Histogram Equalization): clipLimit=2.0, tileGridSize=(8,8)
- Resize: 224×224 pixels, Bilinear interpolation
- Channel: Grayscale → 3-channel RGB (channel replication)
- Format: PNG
3. Normalization
Not applied at save time. Apply ImageNet normalization at DataLoader time:
- mean = [0.485, 0.456, 0.406]
- std = [0.229, 0.224, 0.225]
4. Train/Test Split
- Method: Patient ID-based GroupShuffleSplit (85/15)
- Train: 96,359 images (26,152 patients)
- Test: 15,620 images (4,616 patients)
- Patient overlap: 0 (verified)
5. Cross-Validation
- Method: 5-Fold GroupKFold (Patient-wise) on train set
- Fold column included in
train.csv(values 0–4) - Fold-to-fold patient overlap: 0 (verified)
6. Multi-hot Encoding
14 diseases encoded in alphabetical order:
| Index | Disease |
|---|---|
| 0 | Atelectasis |
| 1 | Cardiomegaly |
| 2 | Consolidation |
| 3 | Edema |
| 4 | Effusion |
| 5 | Emphysema |
| 6 | Fibrosis |
| 7 | Hernia |
| 8 | Infiltration |
| 9 | Mass |
| 10 | Nodule |
| 11 | Pleural_Thickening |
| 12 | Pneumonia |
| 13 | Pneumothorax |
"No Finding" → all zeros [0,0,0,0,0,0,0,0,0,0,0,0,0,0]
Directory Structure
processed/
├── README.md
├── available/
│ ├── images/ # 111,979 preprocessed images (224×224×3 PNG)
│ ├── data.csv # Full metadata + multi-hot encoding
│ ├── train.csv # Train split (96,359 rows, includes fold column)
│ └── test.csv # Test split (15,620 rows)
└── unavailable/
├── images/ # 141 filtered-out original images
└── data.csv # Metadata for filtered images
CSV Columns
| Column | Description |
|---|---|
| Image Index | Filename (e.g., 00000001_000.png) |
| Finding Labels | Pipe-separated labels (e.g., Atelectasis|Effusion) |
| Follow-up # | Follow-up visit number |
| Patient ID | Unique patient identifier |
| Patient Age | Age in years |
| Patient Gender | M or F |
| View Position | PA or AP |
| OriginalImage[Width,Height] | Original image dimensions |
| OriginalImagePixelSpacing[x,y] | Pixel spacing |
| Atelectasis ... Pneumothorax | Multi-hot encoded disease columns (0 or 1) |
| fold | (train.csv only) Cross-validation fold index (0–4) |