积极学习使用可适应的基于任务的优先级
Shaheer U Saeed1, João Ramalhinho1, Mark Pinnock1
1Centre for Medical Image Computing, Wellcome/EPSRC Centre for Interventional & Surgical Sciences, and Department of Medical Physics & Biomedical Engineering, University College London, London, UK.
这项研究引入了一个人工智能控制器,用于在主动学习中对医疗图像进行优先排序,大大减少了对专家注释的需求. 该方法适应新任务,提高细分精度,使用更少的标记数据.
科学领域:
- 医疗图像计算 医疗图像计算
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 用于医学成像的监督机器学习需要广泛的专家注释,这耗时且昂贵.
- 没有标记的医疗图像数据通常是丰富的,为高效的学习策略提供了机会.
研究的目的:
- 开发一种可适应的积极学习策略,用于标签效率高的医疗图像细分.
- 创建一个控制器神经网络,在批量模式的积极学习中对图像进行专家注释的优先级.
主要方法:
- 开发了一个控制器神经网络,用于测量批次内的图像优先级,用于多类细分.
- 控制器使用马尔科夫决策过程 (MDP) 框架内的超强化学习算法进行了优化.
- 该方法使用来自超过一千名患者的CT数据集在九个腹部器官细分任务中进行了验证.
主要成果:
- 拟议的可适应优先级度量实现了对新细分任务的融合细分精度,使用标签比启发式或随机方法少40-60%.
- 观察到显著的性能改善:与随机优先排序相比,脏细分的子得分为22.6%,肝血管细分为10.2%.
- 控制器展示了跨机构和跨机构的适应性,有效地优先考虑图像用于新的细分任务.
结论:
- 开发的meta-reinforcement学习方法使一个可适应的优先级控制器能够实现高效的医疗图像注释.
- 这种方法大大减少了训练准确的医疗图像细分模型所需的标记数据量.
- 可适应的优先级策略显示出强大的潜力,可以提高机器学习在临床环境中的效率和有效性.
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