COVID-19影像数据收集
包含COVID-19及MERS、SARS、ARDS病例的胸部X光和CT图像数据集。
基本信息
资源简介
本数据集收集了COVID-19病例的胸部X光或CT图像，同时也包含MERS、SARS和ARDS等病例的影像数据，用于研究相关疾病的影像学特征。
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。
暂未开放公开下载
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免登录有偿下载
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提供高速下载与技术交付服务（收技术服务费，非数据销售）
暂未开放千方医数集，医疗数据集部分，是为社区服务的公开医疗数据集搜索引擎，并不存储或者下载原始的任何数据。 如果您有其他医疗数据需求，可以和客服联系，或者下工单。我们有强大的三甲医疗机构帮助您提供个性化的医疗数据定制、采集、标注服务。
使用方式
数据集获取
git clone https://github.com/mathewspjacob/covid-chestxray-dataset.git
curl -L -o repo.zip https://github.com/mathewspjacob/covid-chestxray-dataset/archive/refs/heads/master.zip
unzip repo.zip
源站 README 摘录（使用方式）
🛑 Note: please do not claim diagnostic performance of a model without a clinical study! This is not a kaggle competition dataset.
COVID-19 image data collection
We are building a database of COVID-19 cases with chest X-ray or CT images. We are looking for COVID-19 cases as well as MERS, SARS, and ARDS.
All images and data will be released publicly in this GitHub repo. Currently we are building the database with images from publications as they are images that are already available.
View current images and metadata
Current stats of PA, AP, and AP Supine views. Labels 0=No or 1=Yes. Data loader is here
COVID19_Dataset num_samples=136 views=[''PA'']
{''ARDS'': {0.0: 140, 1.0: 5},
''Bacterial Pneumonia'': {0.0: 128, 1.0: 17},
''COVID-19'': {0.0: 46, 1.0: 99},
''Chlamydophila'': {0.0: 144, 1.0: 1},
''Fungal Pneumonia'': {0.0: 132, 1.0: 13},
''Klebsiella'': {0.0: 144, 1.0: 1},
''Legionella'': {0.0: 143, 1.0: 2},
''MERS'': {0.0: 145},
''No Finding'': {0.0: 144, 1.0: 1},
''Pneumocystis'': {0.0: 132, 1.0: 13},
''Pneumonia'': {0.0: 1, 1.0: 144},
''SARS'': {0.0: 134, 1.0: 11},
''Streptococcus'': {0.0: 132, 1.0: 13},
''Viral Pneumonia'': {0.0: 35, 1.0: 110}}
COVID19_Dataset num_samples=28 views=[''AP'', ''AP Supine'']
{''ARDS'': {0.0: 33, 1.0: 1},
''Bacterial Pneumonia'': {0.0: 34},
''COVID-19'': {0.0: 4, 1.0: 30},
''Chlamydophila'': {0.0: 34},
''Fungal Pneumonia'': {0.0: 34},
''Klebsiella'': {0.0: 34},
''Legionella'': {0.0: 34},
''MERS'': {0.0: 34},
''No Finding'': {0.0: 34},
''Pneumocystis'': {0.0: 34},
''Pneumonia'': {0.0: 4, 1.0: 30},
''SARS'': {0.0: 34},
''Streptococcus'': {0.0: 34},
''Viral Pneumonia'': {0.0: 4, 1.0: 30}}
Contribute
- We can extract images from publications. Help identify publications which are not already included using a GitHub issue (DOIs we have are listed in the metadata file). There is a searchable database of COVID-19 papers here, and a non-searchable one (requires download) [here](https://pages.semanticscholar.org/coronavirus-r
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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