方舟+:从许多不同标记的数据集中监督训练单个高性能AI基础模型 - 不需要标签整合
DongAo Ma1, Jiaxuan Pang1, Shivasakthi Senthil Velan1
1School of Computing and Augmented Intelligence, Arizona State University, 1151 S Forest Ave, Tempe, 85281, AZ, USA.
Medical image analysis
|November 30, 2025
概括
一个新的框架,Ark+,使训练强大的人工智能 (AI) 模型使用多样化,异质标记的数据集,而无需手动协调. 这种方法超越了专有模式,使人工智能发展民主化并加速开放科学.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 医疗成像医学成像
背景情况:
- 深度学习模型需要大量的标记数据集来实现高性能,通常需要专有数据.
- 公共可用的医学成像数据集众多,但个别很小,并遭受异质的专家标签.
- 现有的方法很难将这些多样化的数据集汇总起来,用于训练单一的,强大的AI模型.
研究的目的:
- 引入Ark+,一个用于训练单一高性能人工智能 (AI) 模型的新框架,使用多个异构标记的数据集.
- 在没有手动数据协调的情况下,克服监督学习中标签异质性的挑战.
- 展示Ark+在创建可用于各种领域的强大的AI基础模型方面的能力.
主要方法:
- 开发了Ark+,一个框架,旨在从不同数据集的异质专家注释中积累和重复使用知识.
- 预训练的Ark+模型 (Ark+5和Ark+6) 基于聚合的公共胸部X光学数据集 (例如MIMIC-CXR,CheXpert).
- 评估了对分类,细分和本地化任务的Ark+模型,包括对联合学习和多模式应用的废除研究和模拟.
主要成果:
- 与最先进的基线和专有模型 (谷歌的CXR-FM) 相比,Ark+模型表现出优越和强大的性能.
- 废弃性研究证实了Ark+组件的有效性及其对替代策略的优势.
- Ark+显示了可扩展性,建筑的独立性和可扩展性到不同的成像模式 (例如,基金摄影).
结论:
- 在监督学习方面,Ark+提供了方法上的突破,使得从多样化,公开可用的数据中创建强大的AI基础模型成为可能.
- 该框架有效地处理标签异质性,降低注释成本,并使患者群体多样化,以提高模型性能.
- Ark+对开放科学有重大影响,促进了各种科学学科的开放,优越和强大的基础模型的开发.
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