Datasetpublic

arudaev/chest-xray-14

NIH Chest X-ray14 — Processed for CheXVision This dataset wraps the NIH Chest X-ray14 dataset, preprocessed for the CheXVision project. Labels Label Count Prevalence Infiltration 19,894 17.7% Effusion 13,317 11.9% Atelectasis 11,559 10.3% Nodule 6,331 5.6% Mass 5,782 5.2% Pneumothorax 5,302 4.7% Consolidation 4,667 4.2% Pleural_Thickening 3,385 3.0% Cardiomegaly 2,776 2.5% Emphysema 2,516 2.2% Edema 2,303 2.1% Fibrosis 1,686 1.5%… See the full description on the dataset page: https://huggingface.co/datasets/arudaev/chest-xray-14.

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Dataset Card

NIH Chest X-ray14 — Processed for CheXVision

This dataset wraps the NIH Chest X-ray14 dataset, preprocessed for the CheXVision project.

Dataset Description

—Source: NIH Clinical Center
—Images: 112,120 frontal-view chest X-ray images
—Labels: 14 pathological conditions (multi-label)
—Resolution: 1024x1024 (original), resized to 224x224 for training

Labels

Label	Count	Prevalence
Infiltration	19,894	17.7%
Effusion	13,317	11.9%
Atelectasis	11,559	10.3%
Nodule	6,331	5.6%
Mass	5,782	5.2%
Pneumothorax	5,302	4.7%
Consolidation	4,667	4.2%
Pleural_Thickening	3,385	3.0%
Cardiomegaly	2,776	2.5%
Emphysema	2,516	2.2%
Edema	2,303	2.1%
Fibrosis	1,686	1.5%
Pneumonia	1,431	1.3%
Hernia	227	0.2%
No Finding	60,361	53.8%

Usage

python

from datasets import load_dataset

# Load from source
dataset = load_dataset("alkzar90/NIH-Chest-X-ray-dataset")

Tasks

1.Multi-label classification: Predict all 14 pathologies per image
2.Binary classification: Normal (No Finding) vs Abnormal (any pathology)

Citation

bibtex

@inproceedings{wang2017chestx,
  title={ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks},
  author={Wang, Xiaosong and Peng, Yifan and Lu, Le and Lu, Zhiyong and Bagheri, Mohammadhadi and Summers, Ronald M},
  booktitle={CVPR},
  year={2017}
}

Project

Part of the CheXVision project -- Deep Learning & Big Data, AIN.