COVID-19图像数据集合
包含COVID-19、MERS、SARS和ARDS病例的胸部X光或CT图像数据集，用于医学影像研究。
基本信息
资源简介
该数据集收集了COVID-19、MERS、SARS和ARDS病例的胸部X光或CT图像，旨在为医学影像分析研究提供公开资源。数据来源于已发表文献中的公开图像。
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
Tips: 该数据集属于公开下载，应该可以免费公开下载。
免登录有偿下载
Tips: 该数据集 Qianfanghub 可以协助提供有偿下载服务，注意，服务不针对数据相关产权，只是技术服务费。
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/SafwenNaimi/covid-chestxray-dataset.git
curl -L -o repo.zip https://github.com/SafwenNaimi/covid-chestxray-dataset/archive/refs/heads/master.zip
unzip repo.zip
源站 README 摘录（使用方式）
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. Labels 0=No or 1=Yes. Data loader is here
{''ARDS'': {0.0: 75, 1.0: 4},
''Bacterial Pneumonia'': {0.0: 73, 1.0: 6},
''COVID-19'': {0.0: 23, 1.0: 56},
''MERS'': {0.0: 79},
''No Finding'': {0.0: 78, 1.0: 1},
''Pneumonia'': {0.0: 2, 1.0: 77},
''SARS'': {0.0: 68, 1.0: 11},
''Streptococcus'': {0.0: 73, 1.0: 6},
''Viral Pneumonia'': {0.0: 12, 1.0: 67}}
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).
- Submit data to https://radiopedia.org/ or https://www.sirm.org/category/senza-categoria/covid-19/ (we can scrape the data from them)
- Provide bounding box/masks for the detection of problematic regions in images already collected.
Formats: For chest X-ray dcm, jpg, or png are preferred. For CT nifti (in gzip format) is preferred but also dcms. Please contact with any questions.
Motivation
COVID is possibly better diagnosed using radiological imaging Fang, 2020. Companies are developing AI tools and deploying them at hospitals Wired 2020. We should have an open database to develop free tools that will also provide assistance.
Goal
Our goal is to use these images to develop AI based approaches to predict and understand the infection. Our group will work to release these models using the open source Chester AI Radiology Assistant platform.
Contact
Contact: [Joseph Paul Cohen. Postdoctoral Fellow, Mila, University of Montreal](https://jo
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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