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为有效的医学增量学习进行代表性数据选择

Bo-Quan Wei, Jen-Jee Chen, Yu-Chee Tseng

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 12, 2023

概括

本研究介绍了一种使用变量自编码器 (VAE) 和对抗网络进行高效增量学习的新型数据选择方法. 它可以通过新数据不断改进模型,这对于医学成像和缺陷检测至关重要.

科学领域:

人工智能的人工智能
机器学习机器学习
计算机视觉计算机视觉

背景情况:

深度神经网络训练需要大量的注释数据,在医学成像和工业缺陷检测等领域,通常很难一次获得这些数据.
新的数据往往是渐进的,需要模型可以随着时间的推移而适应和改进,而无需完全重新培训.

研究的目的:

为高效的增量学习制定数据选择策略,使模型能够随着新信息的可用性而使用固定数量的数据进行再培训.
为了使模型能够持续改进和适应新数据,同时保持先前学习的信息.

主要方法:

一种混合方法,将变化自编码器 (VAE) 与对抗网络相结合,用于智能数据选择.
将重新训练限制在固定的数据子集上,以实现快速的模型更新.
在LGG细分数据集上进行验证,用于语义细分任务.

主要成果:

基于VAE的数据选择模型与对抗训练相结合,有效地选择了具有代表性和可靠性的数据子集.
实现了时间效率高的增量学习,允许快速的模型再培训和适应.
演示了模型能够从小的,传入的数据批次中不断学习,而不会发生灾难性的遗忘.

结论:

拟议的框架为数据稀缺场景中的增量学习提供了实际解决方案,特别是在医疗图像分析中.
随着新的注释数据变得可用,可以立即可视化模型改进,从而促进更快的临床相关性.
通过智能数据选择和再培训,促进深度学习模型有效地适应不断变化的数据集.

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