IEEE Trans Med Imaging. 2019 May;38(5):1139-1149. doi: 10.1109/TMI.2018.2879369. Epub 2018 Nov 2.
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.
组织病理学检查是当今癌症诊断的金标准。然而，这项任务既耗时又容易出错，因为它需要病理学家进行详细的视觉检查和解释。数字病理学旨在通过提供定量分析数字化组织病理学组织图像的计算机方法来缓解这些问题。这些方法的性能主要依赖于它们使用的特征，因此，它们的成功严格取决于这些特征成功地量化组织病理学领域的能力。基于此动机，本文提出了一种新的无监督特征提取器，用于有效地表示和分类组织病理学组织图像。该特征提取器有三个主要贡献：首先，它基于特定于领域的先验知识，提出了在图像中识别显著子区域的方法，并仅使用这些子区域的特征来量化图像，而不是考虑图像所有位置的特征。其次，它引入了一种新的基于深度学习的技术，通过直接从图像数据中学习一组特征来量化显著子区域，并使用这些量化的分布进行图像表示和分类。为此，所提出的基于深度学习的技术构建了受限玻尔兹曼机（RBM）的深度置信网络，将最后一个 RBM 中隐藏单元节点的激活值定义为特征，并通过无监督方式对这些特征进行聚类来学习量化。第三，该提取器是第一个成功将受限玻尔兹曼机应用于组织病理学图像分析领域的实例。我们在微观结肠组织图像上的实验表明，与其他方法相比，所提出的特征提取器能够更有效地获得更准确的分类结果。
