医生在循环中的放射学主动学习 人工智能工作流程:机遇,挑战和未来方向
Monica Luo1,2, Fereshteh Yousefirizi2, Pouria Rouzrokh3
1Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada.
AJR. American journal of roentgenology
|August 20, 2025
概括
积极学习减少了对放射学人工智能 (AI) 广泛专家标记数据的需求. 这种方法通过识别用于注释的最有信息性的数据来增强人工智能模型的性能和医生协作.
科学领域:
- 放射学
- 人工智能
- 机器学习
背景情况:
- 放射学中的人工智能 (AI) 应用正在扩大,包括图像重建,细分,分类和工作流的优化.
- 训练精确的人工智能模型需要大量专家标记的数据集,而这些数据集的获取成本高且耗时长.
- 在数据有限的情况下,主动学习提供了一个减轻标签要求的解决方案.
研究的目的:
- 探索活跃学习在放射学AI中的应用.
- 突出积极学习在减少对放射学AI模型培训资源需求中的作用.
- 在放射学工作流程中加强医生-AI互动和协作.
主要方法:
- 对放射学AI背景下的积极学习策略的文献审查.
- 讨论主动学习概念及其在放射学任务中的应用.
- 展示使用案例和基于文献的例子.
主要成果:
- 积极学习可以识别人类注释的最有信息的数据,从而减少标签的整体负担.
- 这种有针对性的注释提高了人工智能模型的性能,特别是在受约束的数据集中.
- 积极学习促进了医生循环人工智能系统的发展.
结论:
- 积极学习是有效的放射性人工智能开发的重要策略.
- 积极学习的整合可以优化资源分配和加强医生-人工智能合作.
- 建议进行进一步的研究和实施,以应对挑战并利用机遇.
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