土著医疗中可转诊糖尿病视网膜病变的自动多病变检测

数据集获取（Figshare）

• 源站标题：Automated Multi-Lesion Detection for Referable Diabetic Retinopathy in Indigenous Health Care
• DOI：10.6084/m9.figshare.1390624.v1
• 发布日期：2015-04-22
• 文件数：2 个
• 文件总体积：70.07 KB
• 源站页面：https://figshare.com/articles/dataset/Automated_Multi_Lesion_Detection_for_Referable_Diabetic_Retinopathy_in_Indigenous_Health_Care/1390624

命令行下载

curl -L -o "ground-truth.txt" "https://ndownloader.figshare.com/files/2040431"
curl -L -o "Automated Multi-Lesion Detection for Referable Diabetic Retinopathy in Indigenous Health Care.pdf" "https://ndownloader.figshare.com/files/2042525"

文件清单

• ground-truth.txt（456 B）
• Automated Multi-Lesion Detection for Referable Diabetic Retinopathy in Indigenous Health Care.pdf（69.61 KB）

文件总体积 70.07 KB（源站 API 实测）。

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。

数据说明

Diabetic Retinopathy (DR) is a complication of diabetes mellitus that affects more than one-quarter of the population with diabetes, and can lead to blindness if not discovered in time. An automated screening enables the identification of patients who need further medical attention. This study aimed to classify retinal images of Aboriginal and Torres Strait Islander peoples utilizing an automated computer-based multi-lesion eye screening program for diabetic retinopathy. The multi-lesion classifier was trained on 1,014 images from the São Paulo Eye Hospital and tested on retinal images containing no DR-related lesion, single lesions or multiple types of lesions from the Inala Aboriginal and Torres Strait Islander health care centre. The automated multi-lesion classifier has the potential to enhance the efficiency of clinical practice delivering diabetic retinopathy screening. Our program does not necessitate image samples for training from any specific ethnic group or population being assessed and is independent of image pre- or post-processing to identify retinal lesions. In this Aboriginal and Torres Strait Islander population, the program achieved 100% sensitivity and 88.9% specificity in identifying bright lesions, while detection of red lesions achieved a sensitivity of 67% and specificity of 95%. When both bright and red lesions were present, 100% sensitivity with 88.9% specificity was obtained. All results obtained with this automated screening program meet WHO standards for diabetic retinopathy screening.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.1390624.v1（source: https://figshare.com/articles/dataset/Automated_Multi_Lesion_Detection_for_Referable_Diabetic_Retinopathy_in_Indigenous_Health_Care/1390624）。