The general task of image classification seems to be solved due to the development of modern convolutional neural networks (CNNs). However, the high intraclass variability and interclass similarity of plankton images still prevents the practical identification of morphologically similar organisms. This prevails especially for rare organisms. Every CNN requires a vast amount of manually validated training images which renders it inefficient to train study‐specific classifiers. In most follow‐up studies, the plankton community is different from before and this data set shift (DSS) reduces the correct classification rates. A common solution is to discard all uncertain images and hope that the remains still resemble the true field situation. The intention of this North Sea Video Plankton Recorder (VPR) study is to assess if a combination of a Capsule Neural Network (CapsNet) with probability filters can improve the classification success in applications with DSS. Second, to provide a guideline how to customize automated CNN and CapsNet deep learning image analysis methods according to specific research objectives. In community analyses, our approach achieved a discard of uncertain predictions of only 5%. CapsNet and CNN reach similar precision scores, but the CapsNet has lower recall scores despite similar discard ratios. This is due to a higher discard ratio in rare classes. The recall advantage of the CNN decreases with increasing DSS. We present an alternative method to handle rare classes with a CNN achieving a mean recall of 96% by manually validating an average of 6.5% of the original images.

中文翻译：

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自动浮游生物图像分类—胶囊和过滤器能否帮助应对数据集偏移？

图像分类的一般任务似乎由于现代卷积神经网络（CNN）的发展而得以解决。然而，浮游生物图像的高类内变异性和类间相似性仍然阻止了形态相似生物的实际鉴定。对于稀有生物尤其如此。每个CNN都需要大量手动验证的训练图像，这使得训练研究特定分类器的效率低下。在大多数后续研究中，浮游生物群落与以前不同，这种数据集偏移（DSS）降低了正确的分类率。一种常见的解决方案是丢弃所有不确定的图像，并希望其余图像仍然类似于真实的野外情况。这项北海视频浮游生物记录器（VPR）研究的目的是评估胶囊神经网络（CapsNet）与概率过滤器的组合是否可以提高DSS应用程序中的分类成功率。第二，为如何根据特定研究目标定制自动CNN和CapsNet深度学习图像分析方法提供指导。在社区分析中，我们的方法仅丢弃了5％的不确定性预测。CapsNet和CNN的精度得分相近，但是尽管丢弃率相近，但CapsNet的召回率却较低。这是由于稀有类别中较高的丢弃率。CNN的召回优势随DSS的增加而降低。我们提出了一种替代方法，可通过人工验证平均值6来处理CNN达到96％的平均召回率的稀有类别。