合成COVID-19胸部X光数据集
21,295张合成COVID-19胸部X光图像，用于计算机辅助诊断。
基本信息
资源简介
该数据集包含21,295张合成生成的COVID-19胸部X光图像，这些图像通过算法生成，用于计算机辅助诊断研究。
下载信息
注册下载
Tips: 该数据集需要在对应的数据源网站注册通过后，才能进行数据下载，注册有对应要求，或者需要收费。
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使用方式
数据集获取
git clone https://github.com/hasibzunair/synthetic-covid-cxr-dataset.git
curl -L -o repo.zip https://github.com/hasibzunair/synthetic-covid-cxr-dataset/archive/refs/heads/master.zip
unzip repo.zip
源站 README 摘录（使用方式）
Synthetic COVID-19 Chest X-ray Dataset for Computer-Aided Diagnosis
This repository hosts dataset for our ICML 2021 Workshop on Computational Biology (WCB) paper. A more detailed version titled Synthesis of COVID-19 Chest X-rays using Unpaired Image-to-Image Translation is published in the Journal of Social Network Analysis and Mining (SNAM), Special Issue on Tackling COVID-19 Infodemic.
Resources
- ICML 2021 WCB Paper
- SNAM Journal Paper
- arXiv
The dataset consists of 21,295 synthetic COVID-19 chest X-ray images generated using this algorithm. Dataset is available at this link.
<em>
Figure 1. Illustration of the data generation process based on unpaired image-to-image translation. Chest X-ray images are translated from Non-COVID-19 (i.e. Normal or Pneumonia) to COVID-19 and then back to Non-COVID-19 via cycle-consistency
Here’'s a video of the learning in progress. Top row (Normal CXR, Translated COVID-19 CXR, Reconstructed Normal CXR), bottom row (COVID-19 CXR, Translated Normal CXR, Reconstructed COVID-19 CXR).
</p>
Citation
If you use this dataset in your scientific work, please cite the following:
@article{zunair2021synthesis,
title={Synthesis of {COVID}-19 chest {X}-rays using unpaired image-to-image translation},
author={Zunair, Hasib and Hamza, A Ben},
journal={Social Network Analysis and Mining},
volume={11},
number={1},
pages={112},
year={2021},
publisher={Springer}
}
Result highlights
<p align=“center”>
<a href=“#”><img src=“./media/results.png”></a> <br />
</p>
Acknowledgements
The synthetic dataset was generated using https://github.com/hasibzunair/adversarial-lesions.
<img src=“./media/meme.jpeg” width=“300”>
数据加载示例（图像类）
from PIL import Image
import glob, os
files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)
# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path) # 要求 子目录=类别
目录组织与标注格式以源站说明和下载后实际文件为准。
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