Unsupervised Feature Extraction via Deep Learning for Histopathological Classification of Colon Tissue Images
Unsupervised Feature Extraction via Deep Learning for Histopathological Classification of Colon Tissue Images
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DOI：
10.1109/tmi.2018.2879369
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发表时间：
2019-05-01
影响因子：
10.6
通讯作者：
Gunduz-Demir, Cigdem
中科院分区：
文献类型：
--
作者：
Sari, Can Taylan;Gunduz-Demir, Cigdem
组织病理学检查是当今癌症诊断的金标准。然而，这项任务很耗时，而且容易出错，因为它需要病理学家的详细目测和解释。数字病理学旨在通过提供定量分析数字化组织病理组织图像的计算机化方法来缓解这些问题。这些方法的性能主要取决于它们所使用的特征，因此，它们的成功严格依赖于这些特征通过成功量化组织病理学领域的能力。基于这一动机，本文提出了一种新的无监督特征提取算法，用于组织病理组织图像的有效表示和分类。该特征提取方法有三个主要贡献：第一，基于特定领域的先验知识，识别图像中的显著子区域，并通过只利用这些子区域的特征来量化图像，而不是考虑所有图像位置的特征。其次，引入了一种基于深度学习的新技术，通过提取一组直接从图像数据中学习的特征来量化显著的子区域，并利用这些量化的分布来表示和分类图像。为此，基于深度学习的方法构建了受限Boltzmann机器(RBM)的深度信任网络，将最终RBM中隐含单元节点的激活值定义为特征，并通过对这些特征进行无监督聚类来学习量化。第三，该萃取器是在组织病理图像分析领域成功使用受限Boltzmann机器的第一个例子。我们在显微结肠组织图像上的实验表明，与同类方法相比，所提出的特征提取方法能有效地获得更准确的分类结果。
Histopathological examination is today's gold standard for cancer diagnosis. However, this task is time consuming and prone to errors as it requires a detailed visual inspection and interpretation of a pathologist. Digital pathology aims at alleviating these problems by providing computerized methods that quantitatively analyze digitized histopathological tissue images. The performance of these methods mainly relies on the features that they use, and thus, their success strictly depends on the ability of these features by successfully quantifying the histopathology domain. With this motivation, this paper presents a new unsupervised feature extractor for effective representation and classification of histopathological tissue images. This feature extractor has three main contributions: First, it proposes to identify salient subregions in an image, based on domain-specific prior knowledge, and to quantify the image by employing only the characteristics of these subregions instead of considering the characteristics of all image locations. Second, it introduces a new deep learning-based technique that quantizes the salient subregions by extracting a set of features directly learned on image data and uses the distribution of these quantizations for image representation and classification. To this end, the proposed deep learning-based technique constructs a deep belief network of the restricted Boltzmann machines (RBMs), defines the activation values of the hidden unit nodes in the final RBM as the features, and learns the quantizations by clustering these features in an unsupervised way. Third, this extractor is the first example for successfully using the restricted Boltzmann machines in the domain of histopathological image analysis. Our experiments on microscopic colon tissue images reveal that the proposed feature extractor is effective to obtain more accurate classification results compared to its counterparts.