论文标题
在深层域适应性地堆叠合奏，以进行眼科分类
Stacking Ensemble Learning in Deep Domain Adaptation for Ophthalmic Image Classification
论文作者
论文摘要
鉴于大量具有相似属性但域不同的标记数据的可用性，域的适应性是一种有吸引力的方法。在图像分类任务中，获得足够的标签数据有效。我们提出了一种名为Selda的新方法，用于通过扩展三种域适应方法来堆叠集合学习，以有效解决现实世界中的问题。主要的假设是，当将基本域适应模型组合起来时，我们可以通过利用每个基本模型的能力来获得更准确，更健壮的模型。我们延伸最大平均差异（MMD），低级别编码和相关比对（珊瑚），以计算三个基本模型的适应损失。同样，我们利用一个两双连接的图层网络作为元模型来堆叠这三个表现良好的域适应模型的输出预测，以获得眼科映像分类任务的高精度。使用与年龄相关的眼病研究（AREDS）基准眼科数据集的实验结果证明了该模型的有效性。
Domain adaptation is an attractive approach given the availability of a large amount of labeled data with similar properties but different domains. It is effective in image classification tasks where obtaining sufficient label data is challenging. We propose a novel method, named SELDA, for stacking ensemble learning via extending three domain adaptation methods for effectively solving real-world problems. The major assumption is that when base domain adaptation models are combined, we can obtain a more accurate and robust model by exploiting the ability of each of the base models. We extend Maximum Mean Discrepancy (MMD), Low-rank coding, and Correlation Alignment (CORAL) to compute the adaptation loss in three base models. Also, we utilize a two-fully connected layer network as a meta-model to stack the output predictions of these three well-performing domain adaptation models to obtain high accuracy in ophthalmic image classification tasks. The experimental results using Age-Related Eye Disease Study (AREDS) benchmark ophthalmic dataset demonstrate the effectiveness of the proposed model.
