真实眼底（RF）
包含120对低质量和高质量临床眼底图像，用于真实临床环境下的眼底图像恢复研究。
基本信息
资源简介
真实眼底（RF）
【数据集背景】
本数据集源站标题为《dengzhuo-AI/Real-Fundus》。源站首次发布：2022-06-23。
【数据内容】
源站原始描述：# RFormer: Transformer-based Generative Adversarial Network for Real Fundus Image Restoration on A New Clinical Benchmark (J-BHI 2022) Zhuo Deng, Yuanhao Cai, Lu Chen, Zheng Gong, Qiqi Bao, Xue Yao, Dong Fang, Wenming Yang, Shaochong Zhang, Lan Ma *The first two authors contribute equally to this work* # News + **2022.06.28**: Data, code, and models have been released. 🐌 + **2022.06.22**: Our paper has been accepted by J-BHI 2022. 🐌 *** **Abstract**: *Ophthalmologists have used fundus images to…
【适用方向】
影像类数据可用于医学图像分析模型（检测、分割、分类）的训练与评测，也可用于标注质量与成像参数相关性的研究。
【获取与许可】
源站页面：https://github.com/dengzhuo-AI/Real-Fundus
【使用注意】
· 源站最后修改于 2023-05-07，引用时请注意版本时效。
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载，适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档，适合快速预览和评估数据集质量。
免登录当前数据集主要来源为 Kaggle 公开托管，完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取
git clone https://github.com/dengzhuo-AI/Real-Fundus.git
curl -L -o repo.zip https://github.com/dengzhuo-AI/Real-Fundus/archive/refs/heads/main.zip
unzip repo.zip
源站 README 摘录（使用方式）
RFormer: Transformer-based Generative Adversarial Network for Real Fundus Image Restoration on A New Clinical Benchmark (J-BHI 2022)
Zhuo Deng, Yuanhao Cai, Lu Chen, Zheng Gong, Qiqi Bao, Xue Yao, Dong Fang, Wenming Yang, Shaochong Zhang, Lan Ma
The first two authors contribute equally to this work
News
- 2022.06.28: Data, code, and models have been released. 🐌
- 2022.06.22: Our paper has been accepted by J-BHI 2022. 🐌
Abstract: Ophthalmologists have used fundus images to screen and diagnose eye diseases. However, different equipments and ophthalmologists pose large variations to the quality of fundus images. Low-quality (LQ) degraded fundus images easily lead to uncertainty in clinical screening and generally increase the risk of misdiagnosis. Thus, real fundus image restoration is worth studying. Unfortunately, real clinical benchmark has not been explored for this task so far. In this paper, we investigate the real clinical fundus image restoration problem. Firstly, We establish a clinical dataset, Real Fundus (RF), including 120 low- and high-quality (HQ) image pairs. Then we propose a novel Transformer-based Generative Adversarial Network (RFormer) to restore the real degradation of clinical fundus images. The key component in our network is the Window-based Self-Attention Block (WSAB) which captures non-local self-similarity and long-range dependencies. To produce more visually pleasant results, a Transformer-based discriminator is introduced. Extensive experiments on our clinical benchmark show that the proposed RFormer significantly outperforms the state-of-the-art (SOTA) methods. In addition, experiments of downstream tasks such as vessel segmentation and optic disc/cup detection demonstrate that our proposed RFormer benefits clinical fundus image analysis and applications.
Real Fundus
Real Fundus consists of 120 LQ and HQ clinical fundus image pairs with the spatial size of 2560 $\times$ 2560.
Network Architecture
Comparison with State-of-the-art Methods
This repo is a baseline and toolbox containing 8 algorithms for real fundus images.
We are going to enlarge our model zoo in the future.
Quantitative Comparison on Real Fundus
| Method | Params(M) | FLOPS(G) | PSNR | SSIM | Model ZOO |
|---|---|---|---|---|---|
| [Cofe-Net](https://ieeexplore.ieee.org/abstract/document/928883 |
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。
需要用于商业化训练或医疗器械注册（NMPA/FDA）？
公开数据往往缺乏完整的病理回证与专家标注。千方医数集依托陕川双基地与超十家三甲医院直连，提供完全合规、去标识化、支持注册级质控的专病数据集交易服务。
