基本信息

模态

图像

创建/更新时间

2023-04-13

资源简介

3DG-GA

【数据集背景】

本数据集源站标题为《arXiv Query: search_query=&id_list=2304.06106v1&start=0&max_results=1》。源站首次发布：2023-04-12。

【数据内容】

源站原始描述：In biomedical research and artificial intelligence, access to large, well-balanced, and representative datasets is crucial for developing trustworthy applications that can be used in real-world scenarios. However, obtaining such datasets can be challenging, as they are often restricted to hospitals and specialized facilities. To address this issue, the study proposes to generate highly realistic synthetic faces exhibiting drug abuse traits through augmentation. The proposed method, called "3DG-G…

【主题与分类】

主题标签：cs.AI、cs.CV。

【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://arxiv.org/abs/2304.06106v1

原始链接

http://arxiv.org/abs/2304.06106v1

访问原始数据

官方服务

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陕川双基地 × 超十家三甲医院直连

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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

数据集说明

3DG-GA 对应论文数据集（arXiv 预印本）。

数据获取指引

打开论文页面获取作者与项目信息：https://arxiv.org/abs/2304.06106v1
论文 Data Availability / Code Availability 章节标注了数据实际托管位置；
获取到实际数据链接后，按对应平台标准方式下载。

论文摘要：Abstract:In biomedical research and artificial intelligence, access to large, well-balanced, and representative datasets is crucial for developing trustworthy applications that can be used in real-world scenarios. However, obtaining such datasets can be challenging, as they are often restricted to hospitals and specialized facilities. To address this issue, the study proposes to generate highly realistic synthetic faces exhibiting drug abuse traits through augmentation. The proposed method, called "3DG-GA", Deep De-identified anonymous Dataset Generation, uses Genetics Algorithm as a strategy for synthetic faces generation. The algorithm includes GAN artificial face generation, forgery detection, and face recognition. Initially, a dataset of 120 images of actual facial drug abuse is used. By preserving, the drug traits, the 3DG-GA provides a dataset containing 3000 synthetic facial drug abuse images. The dataset will be open to the scientific community, which can reproduce our results and benefit from the generated datasets while avoiding legal or ethical restrictions.