CystoDS:用于人工智能辅助的膀癌检测的多类内镜图像数据集
Timothy J Lee1,2, Liang Qiu1,2, Jin Long3
1Department of Urology, Stanford University School of Medicine, Stanford, USA.
Scientific data
|February 25, 2026
概括
我们开发了CystoDS,这是一款用于人工智能 (AI) 的大规模膀成像数据集,用于在囊镜检查期间改善膀癌症的检测. 这种AI-ready数据集有助于识别癌症和良性病变,提高诊断准确度.
科学领域:
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 囊镜对于膀癌的检测至关重要,但有其局限性.
- 人工智能 (AI) 可以通过识别感兴趣的区域 (ROI) 来增强囊镜.
- 在这个领域为人工智能培训创建数据集是具有挑战性和耗时的.
研究的目的:
- 介绍CystoDS,一个用于AI应用的高质量的膀成像数据集.
- 为了促进人工智能驱动的膀癌和模仿癌症的良性病变的检测.
- 提供一个AI-ready数据集用于囊镜的临床决策支持.
主要方法:
- 从160名患者获得8067张标准白光囊镜图像.
- 将图像标记为五个类和22个子类,包括768张图像的细分数据.
- 图像采集,数据集结构和技术验证的详细文件.
主要成果:
- CystoDS是一个全面的数据集,准备用于人工智能模型培训.
- 该数据集支持识别恶性和良性膀病变.
- 技术验证证实了数据集对人工智能应用的适用性.
结论:
- CystoDS解决了在人工智能辅助囊镜中需要高质量的数据的需求.
- 这一数据集可以促进人工智能开发,以改善膀癌的诊断.
- 该数据集支持AI在囊镜检查期间区分癌症和良性病变.
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