公共数据集上的定量比较

数据集获取（Figshare）

• 源站标题：Quantitative comparison on public datasets.
• DOI：10.1371/journal.pone.0282416.t002
• 发布日期：2023-03-16
• 文件数：1 个
• 文件总体积：5.63 KB
• 源站页面：https://plos.figshare.com/articles/dataset/Quantitative_comparison_on_public_datasets_/22286455

命令行下载

curl -L -o "Table 2.xls" "https://ndownloader.figshare.com/files/39635632"

文件清单

• Table 2.xls（5.63 KB）

文件总体积 5.63 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

Problem
Low-quality fundus images with complex degredation can cause costly re-examinations of patients or inaccurate clinical diagnosis.
Aim
This study aims to create an automatic fundus macular image enhancement framework to improve low-quality fundus images and remove complex image degradation.
Method
We propose a new deep learning-based model that automatically enhances low-quality retinal fundus images that suffer from complex degradation. We collected a dataset, comprising 1068 pairs of high-quality (HQ) and low-quality (LQ) fundus images from the Kangbuk Samsung Hospital’s health screening program and ophthalmology department from 2017 to 2019. Then, we used these dataset to develop data augmentation methods to simulate major aspects of retinal image degradation and to propose a customized convolutional neural network (CNN) architecture to enhance LQ images, depending on the nature of the degradation. Peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), r-value (linear index of fuzziness), and proportion of ungradable fundus photographs before and after the enhancement process are calculated to assess the performance of proposed model. A comparative evaluation is conducted on an external database and four different open-source databases.
Results
The results of the evaluation on the external test dataset showed an significant increase in PSNR and SSIM compared with the original LQ images. Moreover, PSNR and SSIM increased by over 4 dB and 0.04, respectively compared with the previous state-of-the-art methods (P P = 0.012).
Conclusion
Our enhancement process improves LQ fundus images that suffer from complex degradation significantly. Moreover our customized CNN achieved improved performance over the existing state-of-the-art methods. Overall, our framework can have a clinical impact on reducing re-examinations and improving the accuracy of diagnosis.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0282416.t002（source: https://plos.figshare.com/articles/dataset/Quantitative_comparison_on_public_datasets_/22286455）。