Browsing by Subject "Automated cancer diagnosis"
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Item Open Access Color graphs for automated cancer diagnosis and grading(Institute of Electrical and Electronics Engineers, 2010-03) Altunbay, D.; Cigir, C.; Sokmensuer, C.; Gunduz Demir, C.This paper reports a new structural method to mathematically represent and quantify a tissue for the purpose of automated and objective cancer diagnosis and grading. Unlike the previous structural methods, which quantify a tissue considering the spatial distributions of its cell nuclei, the proposed method relies on the use of distributions of multiple tissue components for the representation. To this end, it constructs a graph on multiple tissue components and colors its edges depending on the component types of their endpoints. Subsequently, it extracts a new set of structural features from these color graphs and uses these features in the classification of tissues. Working with the images of colon tissues, our experiments demonstrate that the color-graph approach leads to 82.65% test accuracy and that it significantly improves the performance of its counterparts. © 2006 IEEE.Item Open Access Deep learning for digital pathology(Bilkent University, 2020-11) Sarı, Can TaylanHistopathological examination is today’s gold standard for cancer diagnosis and grading. However, this task is time consuming and prone to errors as it requires detailed visual inspection and interpretation of a histopathological sample provided on a glass slide under a microscope by an expert pathologist. Low-cost and high-technology whole slide digital scanners produced in recent years have eliminated the disadvantages of physical glass slide samples by digitizing histopathological samples and relocating them to digital media. Digital pathology aims at alleviating the problems of traditional examination approaches by providing auxiliary computerized tools that quantitatively analyze digitized histopathological images. Traditional machine learning methods have proposed to extract handcrafted features from histopathological images and to use these features in the design of a classification or a segmentation algorithm. 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 to successfully quantify the histopathology domain. More recent studies have employed deep architectures to learn expressive and robust features directly from images avoiding complex feature extraction procedures of traditional approaches. Although deep learning methods perform well in many classification and segmentation problems, convolutional neural networks that they frequently make use of require annotated data for training and this makes it difficult to utilize unannotated data that cover the majority of the available data in the histopathology domain. This thesis addresses the challenges of traditional and deep learning approaches by incorporating unsupervised learning into classification and segmentation algorithms for feature extraction and training regularization purposes in the histopathology domain. As the first contribution of this thesis, the first study presents a new unsupervised feature extractor for effective representation and classification of histopathological tissue images. This study introduces a deep belief network to quantize the salient subregions, which are identified with domain-specific prior knowledge, by extracting a set of features directly learned on image data in an unsupervised way and uses the distribution of these quantizations for image representation and classification. As its second contribution, the second study proposes a new regularization method to train a fully convolutional network for semantic tissue segmentation in histopathological images. This study relies on the benefit of unsupervised learning, in the form of image reconstruction, for network training. To this end, it puts forward an idea of defining a new embedding, which is generated by superimposing an input image on its segmentation map, that allows uniting the main supervised task of semantic segmentation and an auxiliary unsupervised task of image reconstruction into a single one and proposes to learn this united task by a generative adversarial network. We compare our classification and segmentation methods with traditional machine learning methods and the state-of-the-art deep learning algorithms on various histopathological image datasets. Visual and quantitative results of our experiments demonstrate that the proposed methods are capable of learning robust features from histopathological images and provides more accurate results than their counterparts.Item Open Access Graph walks for classification of histopathological images(IEEE, 2013) Olgun, Gülden; Sokmensuer, C.; Gündüz-Demir, ÇiğdemThis paper reports a new structural approach for automated classification of histopathological tissue images. It has two main contributions: First, unlike previous structural approaches that use a single graph for representing a tissue image, it proposes to obtain a set of subgraphs through graph walking and use these subgraphs in representing the image. Second, it proposes to characterize subgraphs by directly using distribution of their edges, instead of employing conventional global graph features, and use these characterizations in classification. Our experiments on colon tissue images reveal that the proposed structural approach is effective to obtain high accuracies in tissue image classification. © 2013 IEEE.Item Open Access A hybrid classification model for digital pathology using structural and statistical pattern recognition(Institute of Electrical and Electronics Engineers, 2013) Ozdemir, E.; Gunduz-Demir, C.Cancer causes deviations in the distribution of cells, leading to changes in biological structures that they form. Correct localization and characterization of these structures are crucial for accurate cancer diagnosis and grading. In this paper, we introduce an effective hybrid model that employs both structural and statistical pattern recognition techniques to locate and characterize the biological structures in a tissue image for tissue quantification. To this end, this hybrid model defines an attributed graph for a tissue image and a set of query graphs as a reference to the normal biological structure. It then locates key regions that are most similar to a normal biological structure by searching the query graphs over the entire tissue graph. Unlike conventional approaches, this hybrid model quantifies the located key regions with two different types of features extracted using structural and statistical techniques. The first type includes embedding of graph edit distances to the query graphs whereas the second one comprises textural features of the key regions. Working with colon tissue images, our experiments demonstrate that the proposed hybrid model leads to higher classification accuracies, compared against the conventional approaches that use only statistical techniques for tissue quantification. © 2012 IEEE.Item Open Access A resampling-based Markovian model for automated colon cancer diagnosis(Institute of Electrical and Electronics Engineers, 2012-01) Ozdemir, E.; Sokmensuer, C.; Gunduz Demir, C.In recent years, there has been a great effort in the research of implementing automated diagnostic systems for tissue images. One major challenge in this implementation is to design systems that are robust to image variations. In order to meet this challenge, it is important to learn the systems on a large number of labeled images from a different range of variation. However, acquiring labeled images is quite difficult in this domain, and hence, the labeled training data are typically very limited. Although the issue of having limited labeled data is acknowledged by many researchers, it has rarely been considered in the system design. This paper successfully addresses this issue, introducing a new resampling framework to simulate variations in tissue images. This framework generates multiple sequences from an image for its representation and models them using a Markov process. Working with colon tissue images, our experiments show that this framework increases the generalization capacity of a learner by increasing the size and variation of the training data and improves the classification performance of a given image by combining the decisions obtained on its sequences.Item Open Access Resampling-based Markovian modeling for automated cancer diagnosis(Bilkent University, 2011) Özdemir, ErdemCorrect diagnosis and grading of cancer is very crucial for planning an effective treatment. However, cancer diagnosis on biopsy images involves visual interpretation of a pathologist, which is highly subjective. This subjectivity may, however, lead to selecting suboptimal treatment plans. In order to circumvent this problem, it has been proposed to use automatic diagnosis and grading systems that help decrease the subjectivity levels by providing quantitative measures. However, one major challenge for designing these systems is the existence of high variance observed in the biopsy images due to the nature of biopsies. Thus, for successful classifications of unseen images, these systems should be trained with a large number of labeled images. However, most of the training sets in this domain have limited size of labeled data since it is quite difficult to collect and label histopathological images. In this thesis, we successfully address this issue by presenting a new resampling framework. This framework relies on increasing the generalization capacity of a classifier by augmenting the size and variation in the training set. To this end, we generate multiple sequences from an image, each of which corresponds to a perturbed sample of the image. Each perturbed sample characterizes different parts of the image, and hence, they are slightly different from each other. The use of these perturbed samples for representing the image increases the size and variability of the training set. These samples are modeled with Markov processes which are used to classify unseen image. Working with histopathological tissue images, our experiments demonstrate that the proposed framework is more effective for both larger and smaller training sets compared against other approaches. Additionally, they show that the use of perturbed samples is effective in a voting scheme which boosts the performance of the classifier.Item Open Access Two-tier tissue decomposition for histopathological image representation and classification(Institute of Electrical and Electronics Engineers, 2015) Gultekin, T.; Koyuncu, C. F.; Sokmensuer, C.; Gunduz Demir, C.In digital pathology, devising effective image representations is crucial to design robust automated diagnosis systems. To this end, many studies have proposed to develop object-based representations, instead of directly using image pixels, since a histopathological image may contain a considerable amount of noise typically at the pixel-level. These previous studies mostly employ color information to define their objects, which approximately represent histological tissue components in an image, and then use the spatial distribution of these objects for image representation and classification. Thus, object definition has a direct effect on the way of representing the image, which in turn affects classification accuracies. In this paper, our aim is to design a classification system for histopathological images. Towards this end, we present a new model for effective representation of these images that will be used by the classification system. The contributions of this model are twofold. First, it introduces a new two-tier tissue decomposition method for defining a set of multityped objects in an image. Different than the previous studies, these objects are defined combining texture, shape, and size information and they may correspond to individual histological tissue components as well as local tissue subregions of different characteristics. As its second contribution, it defines a new metric, which we call dominant blob scale, to characterize the shape and size of an object with a single scalar value. Our experiments on colon tissue images reveal that this new object definition and characterization provides distinguishing representation of normal and cancerous histopathological images, which is effective to obtain more accurate classification results compared to its counterparts.Item Open Access Unsupervised feature extraction via deep learning for histopathological classification of colon tissue images(Institute of Electrical and Electronics Engineers, 2019) Sarı, Can Taylan; Gündüz-Demir, ÇiğdemHistopathological 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. IEEE