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
MPOS数据集由香港科技大学（广州）和四川大学华西医院合作创建，包含600对彩色眼底照片（CFP）和荧光素眼底血管造影（FFA）图像，涵盖五种不同的眼底疾病类别。该数据集主要用于多模态眼部图像合成研究，旨在通过非侵入性方法合成FFA图像，以提高眼科疾病的诊断准确性和患者安全性。
下载信息
注册下载
Tips: 该数据集需要在对应的数据源网站注册通过后，才能进行数据下载，注册有对应要求，或者需要收费。
暂未开放公开下载
Tips: 该数据集属于公开下载，应该可以免费公开下载。
免登录有偿下载
Tips: 该数据集 Qianfanghub 可以协助提供有偿下载服务，注意，服务不针对数据相关产权，只是技术服务费。
提供高速下载与技术交付服务（收技术服务费，非数据销售）
暂未开放千方医数集，医疗数据集部分，是为社区服务的公开医疗数据集搜索引擎，并不存储或者下载原始的任何数据。 如果您有其他医疗数据需求，可以和客服联系，或者下工单。我们有强大的三甲医疗机构帮助您提供个性化的医疗数据定制、采集、标注服务。
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。
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