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    科学领域:

    • 医疗图像分析 医学图像分析
    • 医疗保健中的人工智能
    • 计算机视觉 计算机视觉 计算机视觉

    背景情况:

    • 医疗成像的深度学习模型面临由于不同机构的领域转移而导致的性能下降.
    • 损伤检测特别容易受到由器官,疾病,成像设备和协议的变化引起的域转移的影响.
    • 医疗图像的手动注释用于训练深度学习模型是资源密集的,耗时的,需要专家知识.

    研究的目的:

    • 开发一个具有成本效益的框架,以改善在不同领域的医疗图像分析中的深度学习模型性能.
    • 通过智能选择标签样本来减少医学成像中手动数据注释的负担.
    • 为了提高病变检测模型的稳定性和通用性,尽管存在域变异.

    主要方法:

    • 提出了一个域移动主动学习 (DistAL) 框架,将主动学习与域不变特征学习相结合.
    • 使用对比一致性培训来学习歧视性和域不变特征.
    • 引入了RUDY (代表性,不确定性和多样性) 样本选择策略,以实现高效和多样化的数据选择.

    主要成果:

    • 在所有目标域标签上训练的模型中,DistAL的表现与所有目标域标签上训练的模型相比,只在目标样本中注释了1.7%.
    • 鲁迪策略有效地选择了具有代表性,不确定性和多样性的样本,减轻了领域转移挑战.
    • 在来自不同医院的八个不同数据集的五个实验中,表现优于其他积极学习方法.

    结论:

    • 拟议的DistAL框架为医疗图像分析中的域位移问题提供了可行的解决方案,特别是用于病变检测.
    • 将主动学习与域不变特征学习相结合,可以显著降低注释成本,同时保持高模型准确性.
    • 与现有方法相比,DistAL表现出卓越的性能和效率,为医疗保健中更容易获得的人工智能铺平了道路.