When approaching thyroid gland tumor classification, the differentiation between samples with and without "papillary thyroid carcinoma-like" nuclei is a daunting task with high inter-observer variability among pathologists. Thus, there is increasing interest in the use of machine learning approaches to provide pathologists real-time decision support. In this paper, we optimize and quantitatively compare two automated machine learning methods for thyroid gland tumor classification on two datasets to assist pathologists in decision-making regarding these methods and their parameters. The first method is a feature-based classification originating from common image processing and consists of cell nucleus segmentation, feature extraction, and subsequent thyroid gland tumor classification utilizing different classifiers. The second method is a deep learning-based classification which directly classifies the input images with a convolutional neural network without the need for cell nucleus segmentation. On the Tharun and Thompson dataset, the feature-based classification achieves an accuracy of 89.7% (Cohen's Kappa 0.79), compared to the deep learning-based classification of 89.1% (Cohen's Kappa 0.78). On the Nikiforov dataset, the feature-based classification achieves an accuracy of 83.5% (Cohen's Kappa 0.46) compared to the deep learning-based classification 77.4% (Cohen's Kappa 0.35). Thus, both automated thyroid tumor classification methods can reach the classification level of an expert pathologist. To our knowledge, this is the first study comparing feature-based and deep learning-based classification regarding their ability to classify samples with and without papillary thyroid carcinoma-like nuclei on two large-scale datasets.

中文翻译：

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用于自动分类具有乳头状甲状腺癌样细胞核的肿瘤的机器学习方法：定量分析。

在接近甲状腺肿瘤分类时，区分具有和不具有“甲状腺乳头状癌样”细胞核的样本是一项艰巨的任务，病理学家之间的观察者间差异很大。因此，人们对使用机器学习方法为病理学家提供实时决策支持越来越感兴趣。在本文中，我们优化并定量比较了两个数据集上甲状腺肿瘤分类的两种自动化机器学习方法，以帮助病理学家就这些方法及其参数做出决策。第一种方法是基于特征的分类，起源于常见的图像处理，由细胞核分割、特征提取和随后利用不同分类器的甲状腺肿瘤分类组成。第二种方法是基于深度学习的分类，它直接用卷积神经网络对输入图像进行分类，不需要细胞核分割。在 Tharun 和 Thompson 数据集上，基于特征的分类准确率为 89.7%（Cohen's Kappa 0.79），而基于深度学习的分类准确率为 89.1%（Cohen's Kappa 0.78）。在 Nikiforov 数据集上，基于特征的分类准确率为 83.5%（Cohen's Kappa 0.46），而基于深度学习的分类准确率为 77.4%（Cohen's Kappa 0.35）。因此，两种自动甲状腺肿瘤分类方法都可以达到专家病理学家的分类水平。据我们所知，