胃网-5M:用于开发胃肠内镜基础模型的多中心数据集
Martijn R Jong1, Tim G W Boers2, Kiki N Fockens1
1Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, the Netherlands.
一个新的内镜图像的大型数据集,GastroNet-5M,使人工智能 (AI) 在内镜中的基础模型成为可能. 这提高了诊断准确度,并减少了人工智能开发的数据需求.
科学领域:
- 医疗人工智能 医疗人工智能
- 计算机视觉在医学中的应用
- 胃肠病学成像成像 胃肠病学成像
背景情况:
- 对于内镜来说,深度学习需要大量的注释数据集,这些数据集往往稀缺且昂贵.
- 基础模型,在各种数据上进行预训练,提供了一个解决方案,但需要一般的内镜图像数据集.
- 开发内镜基础模型的现有数据集是有限的.
研究的目的:
- 介绍GastroNet-5M,这是一个大规模的一般内镜图像数据集.
- 开发和评估使用GastroNet-5M的内镜人工智能 (AI) 应用程序的基础模型.
- 评估GastroNet-5M基础模型与现有模型的性能.
主要方法:
- 编制的GastroNet-5M数据集:来自8家医院的4,820,653张匿名内镜图像 (2012-2020年).
- 利用自我监督学习在GastroNet-5M上预训练一个基础模型.
- 将GastroNet-5M基础模型与17个AI应用中的其他模型进行比较,测量精度,数据效率和稳定性.
主要成果:
- 用GastroNet-5M预训练的模型在大多数分类和细分任务中实现了卓越的准确性.
- 这些模型所需的应用特定培训数据要少得多.
- 当遇到来自不同内镜制造商的数据异质性时,证明了增强的稳定性.
结论:
- GastroNet-5M,一个包含500万张内镜图像的数据集,有助于开发先进的内镜AI.
- 使用GastroNet-5M进行预训练可以提高诊断准确度,减少数据需求,并在临床环境中提高AI的稳定性.
- 盖斯特罗Net-5M的公共可用性旨在加速内镜AI系统的开发和实施.
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