使用个人健康数据进行卵巢癌风险分层
使用神经网络基于个人健康数据预测卵巢癌风险并进行风险分层的图像结果数据集。
基本信息
资源简介
使用个人健康数据进行卵巢癌风险分层
【一句话概览】
本数据集是 figshare 公开的影像数据集，收录 1 个文件、合计 327.98 KB，主题涵盖 ovarian cancer、neural network、risk stratification、personal health data、screening，可用于医学图像分割、检测与分类模型的训练与评测，CC BY 4.0 许可开放获取。
【数据集背景】
本数据集源站标题为《fdata-02-00024-g0004_Stratifying Ovarian Cancer Risk Using Personal Health Data.tif》。配套论文《Committee Opinion No. 716: The Role of the Obstetrician–Gynecologist in the Early Detection of Epithelial Ovarian Cancer in Women at Average Risk》发表于 Obstetrics & Gynecology（2017），DOI：10.1097/AOG.0000000000002299。源站首次发布：2020-03-06。
【数据内容】
源站原始描述：Purpose: Screening the general population for ovarian cancer is not recommended by every major medical or public health organization because the harms from screening outweigh the benefit it provides. To improve ovarian cancer detection and survival many are looking at high-risk populations who would benefit from screening. Methods: We train a neural network on readily available personal health data to predict and stratify ovarian cancer risk. We use two different datasets to train our network: T…
【数据结构与技术规格】
文件数 1 个，合计 327.98 KB。fdata-02-00024-g0004_Stratifying Ovarian Cancer Risk Using Personal Health Data.tif：image/tiff，327.98 KB。数据集 DOI：10.3389/fdata.2019.00024.s006。
【主题与分类】
源站学科分类：Data Structures、Artificial Intelligence and Image Processing、Computer Engineering、Applied Computer Science、Pattern Recognition and Data Mining。主题标签：ovarian cancer、neural network、risk stratification、personal health data、screening。
【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://frontiersin.figshare.com/articles/dataset/fdata-02-00024-g0004_Stratifying_Ovarian_Cancer_Risk_Using_Personal_Health_Data_tif/11948277
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Hart, Gregory R.; Nartowt, Bradley J.; Muhammad, Wazir; Liang, Ying; Huang, Gloria S.; Deng, Jun (2020). fdata-02-00024-g0004_Stratifying Ovarian Cancer Risk Using Personal Health Data.tif. Frontiers. Dataset
【使用注意】
· 源站最后修改于 2023-06-09，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（Figshare）
- 源站标题：fdata-02-00024-g0004_Stratifying Ovarian Cancer Risk Using Personal Health Data.tif
- DOI：10.3389/fdata.2019.00024.s006
- 发布日期：2020-03-06
- 文件数：1 个
- 文件总体积：327.98 KB
- 源站页面：https://frontiersin.figshare.com/articles/dataset/fdata-02-00024-g0004_Stratifying_Ovarian_Cancer_Risk_Using_Personal_Health_Data_tif/11948277
命令行下载
curl -L -o "fdata-02-00024-g0004_Stratifying Ovarian Cancer Risk Using Personal Health Data.tif" "https://ndownloader.figshare.com/files/21932556"
文件清单
fdata-02-00024-g0004_Stratifying Ovarian Cancer Risk Using Personal Health Data.tif（327.98 KB）
文件总体积 327.98 KB（源站 API 实测）。
数据说明
Purpose: Screening the general population for ovarian cancer is not recommended by every major medical or public health organization because the harms from screening outweigh the benefit it provides. To improve ovarian cancer detection and survival many are looking at high-risk populations who would benefit from screening.
Methods: We train a neural network on readily available personal health data to predict and stratify ovarian cancer risk. We use two different datasets to train our network: The National Health Interview Survey and Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial.
Results: Our model has an area under the receiver operating characteristic curve of 0.71. We further demonstrate how the model could be used to stratify patients into different risk categories. A simple 3-tier scheme classifies 23.8% of those with cancer and 1.0% of those without as high-risk similar to genetic testing, and 1.1% of those with cancer and 24.4% of those without as low risk.
Conclusion: The developed neural network offers a cost-effective and non-invasive way to identify those who could benefit from targeted screening.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.3389/fdata.2019.00024.s006（source: https://frontiersin.figshare.com/articles/dataset/fdata-02-00024-g0004_Stratifying_Ovarian_Cancer_Risk_Using_Personal_Health_Data_tif/11948277）。
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。
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