Figure 1 COVID-19 胸部X光数据集
Figure 1 COVID-19胸部X光数据集，提供COVID-19患者的胸部X光影像，用于检测和风险分层模型研究。
基本信息
模态
医学影像
创建/更新时间
2023-09-26
资源简介
该数据集包含COVID-19患者的胸部X光影像，用于增强COVID-19检测模型（COVID-Net）和风险分层模型（COVID-RiskNet）的研究，是COVIDx数据集的一部分。
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
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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/agchung/Figure1-COVID-chestxray-dataset.git
curl -L -o repo.zip https://github.com/agchung/Figure1-COVID-chestxray-dataset/archive/refs/heads/master.zip
unzip repo.zip
源站 README 摘录（使用方式）
Figure 1 COVID-19 Chest X-ray Dataset Initiative
Note: The COVID-19 image data provided here are intended to be used for research purposes only, and we are working continuously to grow this dataset as new data becomes available.
Core COVID-Net Team
- DarwinAI Corp., Canada and Vision and Image Processing Research Group, University of Waterloo, Canada
- Linda Wang
- Alexander Wong
- Zhong Qiu Lin
- Paul McInnis
- Audrey Chung
- Hayden Gunraj, COVIDNet for CT: Coming soon.
- Vision and Image Processing Research Group, University of Waterloo, Canada
- James Lee
- Matt Ross and Blake VanBerlo (City of London), COVID-19 Chest X-Ray Model: https://github.com/aildnont/covid-cxr
- Ashkan Ebadi (National Research Council Canada)
- Kim-Ann Git (Selayang Hospital)
- Abdul Al-Haimi
We especially thank Figure 1 for their collaboration in compiling this medical data.
We are building this dataset as a part of the COVIDx dataset to enhance our models for COVID-19 detection (COVID-Net) and COVID-19 risk stratification (COVID-RiskNet): - COVID-Net: https://github.com/lindawangg/COVID-Net
- COVID-RiskNet: coming soon
Please see the main COVID-Net repo for details on data extraction and instructions for creating the full COVIDx dataset.
If you would like to contribute COVID-19 x-ray images, please submit them via Figure 1 here. If you have any questions, please contact us at audrey@darwinai.ca and a28wong@uwaterloo.ca or alex@darwinai.ca. Lets all work together to stop the spread of COVID-19!
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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