Department of IT Convergence and Application Engineering, Pukyong National University, Busan 48513, Korea.
Department of Computer Engineering, Dong-A University, Busan 49315, Korea.
Sensors (Basel). 2021 Feb 20;21(4):1469. doi: 10.3390/s21041469.
In computer-aided diagnosis (CAD) systems, the automatic classification of the different types of the human epithelial type 2 (HEp-2) cells represents one of the critical steps in the diagnosis procedure of autoimmune diseases. Most of the methods prefer to tackle this task using the supervised learning paradigm. However, the necessity of having thousands of manually annotated examples constitutes a serious concern for the state-of-the-art HEp-2 cells classification methods. We present in this work a method that uses active learning in order to minimize the necessity of annotating the majority of the examples in the dataset. For this purpose, we use cross-modal transfer learning coupled with parallel deep residual networks. First, the parallel networks, which take simultaneously different wavelet coefficients as inputs, are trained in a fully supervised way by using a very small and already annotated dataset. Then, the trained networks are utilized on the targeted dataset, which is quite larger compared to the first one, using active learning techniques in order to only select the images that really need to be annotated among all the examples. The obtained results show that active learning, when mixed with an efficient transfer learning technique, can allow one to achieve a quite pleasant discrimination performance with only a few annotated examples in hands. This will help in building CAD systems by simplifying the burdensome task of labeling images while maintaining a similar performance with the state-of-the-art methods.
在计算机辅助诊断 (CAD) 系统中，自动分类不同类型的人类上皮细胞 2 型 (HEp-2) 是自身免疫性疾病诊断过程中的关键步骤之一。大多数方法倾向于使用监督学习范例来解决这个任务。然而，需要数千个手动标注的示例这一事实是当前最先进的 HEp-2 细胞分类方法的一个严重问题。在这项工作中，我们提出了一种使用主动学习的方法，以最小化在数据集中标注大多数示例的必要性。为此，我们使用跨模态迁移学习和并行深度残差网络。首先，并行网络同时将不同的小波系数作为输入，通过使用一个非常小且已经标注的数据集进行完全监督训练。然后，利用主动学习技术，将训练好的网络应用于目标数据集，该数据集比第一个数据集大得多，以仅选择在所有示例中真正需要标注的图像。所获得的结果表明，主动学习与高效的迁移学习技术相结合，可以仅使用少数几个标注的示例就能获得相当不错的判别性能。这将有助于通过简化图像标注的繁重任务来构建 CAD 系统，同时保持与最先进方法类似的性能。
