Unsupervised Feature Extraction via Deep Learning for Histopathological Classification of Colon Tissue Images

Abstract

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 rely on features that they use, and thus, their success strictly depends on the ability of these features 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 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 of successfully using 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.

Accurate Diagnosis of Colorectal Cancer Based on Histopathology Images Using Artificial Intelligence

“…There have been many successful applications of DL, specifically convolutional neural network (CNN), in WSI analysis for cancers of, e.g., lung 8,9 , breast 10,11 , prostate 12 , and skin 13,14 . Most of the existing CNN for the CRC WSI analysis focused on the pathology work after cancer determination, including grade classification 15 , tumor cells detection and classification 16-18 , survivorship prediction 19-21 , etc. Although they resulted in reasonably high accuracy, their study sample sizes are limited and hence not fully represent the numerous histologic variants of CRC that have been defined, including tubular, mucinous, signet ring cell, and others 22 .…”

Artificial intelligence in dermatopathology: Diagnosis, education, and research

“…The applications of machine learning in pathology are expanding rapidly, with recent advances in anatomic pathology, including breast, and prostate pathology 1‐5 . Based on trained convolutional neural networks, deep learning algorithms can automate mitotic counting in invasive breast carcinoma, with high concordance to manual counting 1 .…”

Research on the Auxiliary Classification and Diagnosis of Lung Cancer Subtypes Based on Histopathological Images

“…It has developed into a valuable method [15]. Although the above imaging examination methods play an important role in detecting of LC, the results of each examination are only used as a reference for the diagnosis, staging, re-staging, efficacy monitoring and prognosis evaluation of LC, while histopathological examination is the gold standard for tumor qualitative and clinical staging [16,17]. However, due to the complex texture features of histopathological images, as far as the authors know, there is no computer-aided diagnosis method for ASC, LUSC and SCLC based on histopathological images.…”