多病种配对眼部合成数据集（MPOS）
MPOS数据集包含600对彩色眼底照片和荧光素眼底血管造影图像，涵盖五种眼底疾病，用于多模态眼部图像合成研究。
基本信息
资源简介
多病种配对眼部合成数据集（MPOS）
【数据集背景】
本数据集源站标题为《whq-xxh/FFA-Synthesis》。源站首次发布：2024-08-20。
【数据内容】
源站原始描述：FFA Synthesis from CFP （ACM MM 2024 Workshop Best Paper Award） # Non-Invasive to Invasive: Enhancing FFA Synthesis from CFP with a Benchmark Dataset and a Novel Network ACM MM 2024 Multimedia Computing for Health and Medicine Workshop Excited for the Best Paper Award! 🏆 🏆 🏆 Thanks to all the collaborators！ # Dataset 📊 We mentioned our efforts to construct a relevant dataset. We are pleased to offer access to this Multi-disease Paired Ocular Synthesis (MPOS) dataset. We invite researchers working…
【主题与分类】
主题标签：dataset、diabetic-retinopathy、fluorescein-angiography、fundus-image、image-generation、image-systhesis。
【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/whq-xxh/FFA-Synthesis
【使用注意】
· 源站最后修改于 2024-12-13，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。
免登录有偿下载
公开数据集受托下载与技术交付服务。
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/whq-xxh/FFA-Synthesis.git
curl -L -o repo.zip https://github.com/whq-xxh/FFA-Synthesis/archive/refs/heads/main.zip
unzip repo.zip
源站 README 摘录（使用方式）
Non-Invasive to Invasive: Enhancing FFA Synthesis from CFP with a Benchmark Dataset and a Novel Network
ACM MM 2024 Multimedia Computing for Health and Medicine Workshop
Excited for the Best Paper Award! 🏆 🏆 🏆
Thanks to all the collaborators！
Dataset 📊
We mentioned our efforts to construct a relevant dataset. We are pleased to offer access to this Multi-disease Paired Ocular Synthesis (MPOS)
dataset. We invite researchers working on Image synthesis, Retinal Disease Diagnosis, and Medical Imaging and Analysis to make use of this valuable resource.
Busy recently, the code will be organized.
Please contact Hongqiu (hongqiuwang16@gmail.com) for the dataset. One step is needed to download the dataset: **1) Use your google email to apply for the download permission (OneDrive BaiduPan). We just handle the real-name email and your email suffix must match your affiliation. The email should contain the following information:
Name/Homepage/Google Scholar: (Tell us who you are.)
Primary Affiliation: (The name of your institution or university, etc.)
Job Title: (E.g., Professor, Associate Professor, Ph.D., etc.)
Affiliation Email: (the password will be sent to this email, we just reply to the email which is the end of “edu”.)
How to use: (Only for academic research, not for commercial use or second-development.)
The data provided cannot be forwarded to others, and only individuals with approved applications are authorized to use them.
Thanks for understanding and cooperation!
Citation 📖
If you find our work useful or relevant to your research, please consider citing:
@inproceedings{wang2024non,
title={Non-Invasive to Invasive: Enhancing FFA Synthesis from CFP with a Benchmark Dataset and a Novel Network},
author={Wang, Hongqiu and Xing, Zhaohu and Wu, Weitong and Yang, Yijun and Tang, Qingqing and Zhang, Meixia and Xu, Yanwu and Zhu, Lei},
booktitle={Proceedings of the 1st International Workshop on Multimedia Computing for Health and Medicine},
pages={715},
year={2024}
}
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。
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
⚡️ 需要数据支持或标注服务？立即联系我们获取专业方案。
