论文标题
在医学图像分析中积极学习的自信核心
Confident Coreset for Active Learning in Medical Image Analysis
论文作者
论文摘要
深度学习的最新进展已在各种应用中取得了巨大的成功。尽管已广泛研究了半监督或无监督的学习方法，但深度神经网络的性能高度取决于带注释的数据。问题在于，注释预算通常由于注释时间和医疗数据的昂贵注释成本而受到限制。主动学习是该问题的解决方案之一，在该问题中，主动学习者的设计以指示需要注释哪些样本才能有效训练目标模型。在本文中，我们提出了一种新颖的主动学习方法，自信的核心，该方法认为有效选择信息样本的不确定性和分布。通过对两个医学图像分析任务的比较实验，我们表明我们的方法表现优于其他主动学习方法。
Recent advances in deep learning have resulted in great successes in various applications. Although semi-supervised or unsupervised learning methods have been widely investigated, the performance of deep neural networks highly depends on the annotated data. The problem is that the budget for annotation is usually limited due to the annotation time and expensive annotation cost in medical data. Active learning is one of the solutions to this problem where an active learner is designed to indicate which samples need to be annotated to effectively train a target model. In this paper, we propose a novel active learning method, confident coreset, which considers both uncertainty and distribution for effectively selecting informative samples. By comparative experiments on two medical image analysis tasks, we show that our method outperforms other active learning methods.
