论文标题
多级图像分类的深度增强积极学习
Deep reinforced active learning for multi-class image classification
论文作者
论文摘要
高精度的医疗图像分类可以受到获取更多数据以及标记现有图像所需的时间和专业知识的成本的限制。在本文中，我们将主动学习应用于医学图像分类，该方法旨在从较大的数据库中最大程度地提高模型性能。我们提出了一个基于深度强化学习的新的活跃学习框架，以根据卷积神经网络的预测来学习一种主动学习查询策略，以标记图像。我们的框架修改了深Q网络公式，使我们能够在分类器的潜在空间中添加基于几何参数的数据，从而在基于批处理的主动学习设置中进行高精度的多级分类，从而使代理能够标记既有多样化的数据，又是最不确定的。我们将框架应用于两个医学成像数据集，并与标准查询策略以及最新的基于强化学习的主动学习方法进行比较。
High accuracy medical image classification can be limited by the costs of acquiring more data as well as the time and expertise needed to label existing images. In this paper, we apply active learning to medical image classification, a method which aims to maximise model performance on a minimal subset from a larger pool of data. We present a new active learning framework, based on deep reinforcement learning, to learn an active learning query strategy to label images based on predictions from a convolutional neural network. Our framework modifies the deep-Q network formulation, allowing us to pick data based additionally on geometric arguments in the latent space of the classifier, allowing for high accuracy multi-class classification in a batch-based active learning setting, enabling the agent to label datapoints that are both diverse and about which it is most uncertain. We apply our framework to two medical imaging datasets and compare with standard query strategies as well as the most recent reinforcement learning based active learning approach for image classification.
