COVID-19胸部X光数据集
包含6500张胸片X光图像，具有像素级肺部分割，517例COVID-19病例，用于肺炎检测和分割研究。
基本信息
资源简介
该数据集包含6500张AP/PA胸片X光图像，具有像素级的多边形肺部分割，其中517例为COVID-19病例。每张图像提供两个肺部分割掩码（含心脏后区域），肺炎类型标签（病毒性、细菌性、真菌性、健康/无），以及COVID-19患者的年龄、性别、体温、位置、插管状态、ICU入院和患者结果等额外信息。注释以多边形边界表示，可导出为COCO、VOC或Darwin JSON格式。
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/v7labs/covid-19-xray-dataset.git
curl -L -o repo.zip https://github.com/v7labs/covid-19-xray-dataset/archive/refs/heads/master.zip
unzip repo.zip
源站 README 摘录（使用方式）
COVID-19 xray dataset
BROWSE & DOWNLOAD THE DATASET ON V7 DARWIN HERE
or run the following command from Darwin-py to download the latest version
pip install darwin-py
darwin dataset pull v7-labs/covid-19-chest-x-ray-dataset:all-images
This dataset contains 6500 images of AP/PA chest x-rays with pixel-level polygonal lung segmentations. There are 517 cases of COVID-19 amongst these.
Use the command below to download only images presenting COVID-19.
darwin dataset pull v7-labs/covid-19-chest-x-ray-dataset:covid-only
WARNING: This dataset is not intended for use in clinical diagnostics.
Each image contains:
- Two “Lung” segmentation masks (rendered as polygons, including the posterior region behind the heart).
- A tag for the type of pneumonia (viral, bacterial, fungal, healthy/none)
- If the patient has COVID-19, additional tags stating age, sex, temperature, location, intubation status, ICU admission, and patient outcome.
Lung annotations are polygons following pixel-level boundaries. These can be exported asCOCO,VOC, orDarwin JSONformats. Each annotation file contains a URL to the original full resolution image, as well as a reduced size thumbnail.
LUNG SEGMENTATION NOTES: Lung segmentations in this dataset include most of the heart, revealing lung opacities behind the heart which may be relevant for assessing the severity of viral pneumonia. Uniformly shaped lungs also de-couples the shape and content within the left lung from the size of the heart.
The lower-most part of the lungs is defined by the extent of the diaphragm, where visible. If the back of the lungs is clearly visible through the diaphragm it is also included up until the lower-most visible part of the lungs.
Lung segmentations were performed by human annotators using Auto-Annotate, adjusted, and reviewed by humans.
Other important notes:
- Image resolutions, sources, and orientations vary across the dataset, with the largest image being 5600x4700 and smallest being 156x156. You may sort images by dimensions on [Darwin](https://darwin
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。
精度瓶颈？数据缺失？
当前公开数据无法满足您的算法精度？千方提供针对 新冠病毒感染 的高质量、多模态真实临床数据定制解决方案。
