Data Sheet 1：使用端到端深度融合模型与解剖地标可视化在边缘计算设备上进行糖尿病性黄斑水肿检测（PDF）

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_Diabetic Macular Edema Detection Using End-to-End Deep Fusion Model and Anatomical Landmark Visualization on an Edge Computing Device.pdf
• DOI：10.3389/fmed.2022.851644.s001
• 发布日期：2022-04-04
• 文件数：1 个
• 文件总体积：331.95 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Diabetic_Macular_Edema_Detection_Using_End-to-End_Deep_Fusion_Model_and_Anatomical_Landmark_Visualization_on_an_Edge_Computing_Device_pdf/19503244

命令行下载

curl -L -o "Data_Sheet_1_Diabetic Macular Edema Detection Using End-to-End Deep Fusion Model and Anatomical Landmark Visualization on an Edge Computing Device.pdf" "https://ndownloader.figshare.com/files/34660366"

文件清单

• Data_Sheet_1_Diabetic Macular Edema Detection Using End-to-End Deep Fusion Model and Anatomical Landmark Visualization on an Edge Computing Device.pdf（331.95 KB）

文件总体积 331.95 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

PurposeDiabetic macular edema (DME) is a common cause of vision impairment and blindness in patients with diabetes. However, vision loss can be prevented by regular eye examinations during primary care. This study aimed to design an artificial intelligence (AI) system to facilitate ophthalmology referrals by physicians.
MethodsWe developed an end-to-end deep fusion model for DME classification and hard exudate (HE) detection. Based on the architecture of fusion model, we also applied a dual model which included an independent classifier and object detector to perform these two tasks separately. We used 35,001 annotated fundus images from three hospitals between 2007 and 2018 in Taiwan to create a private dataset. The Private dataset, Messidor-1 and Messidor-2 were used to assess the performance of the fusion model for DME classification and HE detection. A second object detector was trained to identify anatomical landmarks (optic disc and macula). We integrated the fusion model and the anatomical landmark detector, and evaluated their performance on an edge device, a device with limited compute resources.
ResultsFor DME classification of our private testing dataset, Messidor-1 and Messidor-2, the area under the receiver operating characteristic curve (AUC) for the fusion model had values of 98.1, 95.2, and 95.8%, the sensitivities were 96.4, 88.7, and 87.4%, the specificities were 90.1, 90.2, and 90.2%, and the accuracies were 90.8, 90.0, and 89.9%, respectively. In addition, the AUC was not significantly different for the fusion and dual models for the three datasets (p = 0.743, 0.942, and 0.114, respectively). For HE detection, the fusion model achieved a sensitivity of 79.5%, a specificity of 87.7%, and an accuracy of 86.3% using our private testing dataset. The sensitivity of the fusion model was higher than that of the dual model (p = 0.048). For optic disc and macula detection, the second object detector achieved accuracies of 98.4% (optic disc) and 99.3% (macula). The fusion model and the anatomical landmark detector can be deployed on a portable edge device.
ConclusionThis portable AI system exhibited excellent performance for the classification of DME,

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fmed.2022.851644.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Diabetic_Macular_Edema_Detection_Using_End-to-End_Deep_Fusion_Model_and_Anatomical_Landmark_Visualization_on_an_Edge_Computing_Device_pdf/19503244）。