Now showing items 1-17 of 17

    • Automated cell analysis in microscopy images 

      Koyuncu, Can Fahrettin (Bilkent University, 2018-09)
      High-throughput microscopy systems have become popular recently, which facilitate to acquire boundless microscopy images without requiring human intervention. However, the analysis of such amount of images using conventional ...
    • Boosting fully convolutional networks for gland instance segmentation in histopathological images 

      Güneşli, Gözde Nur (Bilkent University, 2019-08)
      In the current literature, fully convolutional neural networks (FCNs) are the most preferred architectures for dense prediction tasks, including gland segmentation. However, a signi cant challenge is to adequately train ...
    • Color graph representation for structural analysis of tissue images 

      Altunbay, Doğan (Bilkent University, 2010)
      Computer aided image analysis tools are becoming increasingly important in automated cancer diagnosis and grading. They have the potential of assisting pathologists in histopathological examination of tissues, which may ...
    • Constrained Delaunay triangulation for diagnosis and grading of colon cancer 

      Erdoğan, Süleyman Tuncer (Bilkent University, 2009)
      In our century, the increasing rate of cancer incidents makes it inevitable to employ computerized tools that aim to help pathologists more accurately diagnose and grade cancerous tissues. These mathematical tools offer ...
    • Deep convolutional network for tumor bud detection 

      Koç, Soner (Bilkent University, 2019-04)
      The existence of tumor buds is accepted as a promising biomarker for staging colorectal carcinomas. In the current practice of medicine, these tumor buds are detected by the manual examination of a immunohistochemically ...
    • Deep learning based cell segmentation in histopathological images 

      Doğan, Deniz (Bilkent University, 2018-08)
      In digital pathology, cell imaging systems allow us to comprehend histopathological events at the cellular level. The first step in these systems is generally cell segmentation, which substantially affects the subsequent ...
    • Deep learning based unsupervised tissue segmentation in histopathological images 

      Köylü, Troya Çağıl (Bilkent University, 2017-11)
      In the current practice of medicine, histopathological examination of tissues is essential for cancer diagnosis. However, this task is both subject to observer variability and time consuming for pathologists. Thus, it ...
    • Histopathological image classification using salient point patterns 

      Çığır, Celal (Bilkent University, 2011)
      Over the last decade, computer aided diagnosis (CAD) systems have gained great importance to help pathologists improve the interpretation of histopathological tissue images for cancer detection. These systems offer ...
    • Local object patterns for tissue image representation and cancer classification 

      Olgun, Gülden (Bilkent University, 2013)
      Histopathological examination of a tissue is the routine practice for diagnosis and grading of cancer. However, this examination is subjective since it requires visual interpretation of a pathologist, which mainly depends ...
    • Object-oriented testure analysis and unsupervised segmentation for histopathological images 

      Tosun, Akif Burak (Bilkent University, 2012)
      The histopathological examination of tissue specimens is essential for cancer diagnosis and grading. However, this examination is subject to a considerable amount of observer variability as it mainly relies on visual ...
    • Perceptual watersheds for cell segmentation in fluorescence microscopy images 

      Arslan, Salim (Bilkent University, 2012)
      High content screening aims to analyze complex biological systems and collect quantitative data via automated microscopy imaging to improve the quality of molecular cellular biology research in means of speed and accuracy. ...
    • Qualitative test-cost sensitive classification 

      Cebe, Mümin (Bilkent University, 2008)
      Decision making is a procedure for selecting the best action among several alternatives. In many real-world problems, decision has to be taken under the circumstances in which one has to pay to acquire information. In ...
    • Resampling-based Markovian modeling for automated cancer diagnosis 

      Özdemir, Erdem (Bilkent University, 2011)
      Correct 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 ...
    • Rule based segmentation of colon glands 

      Yücel, Simge (Bilkent University, 2018-09)
      Colon adenocarcinoma, which accounts for more than 90 percent of all colorectal cancers, originates from epithelial cells that form colon glands. Thus, for its diagnosis and grading, it is important to examine the ...
    • Segmentation of colon glands by object graphs 

      Kandemir, Melih (Bilkent University, 2008)
      Histopathological examination is the most frequently used technique for clinical diagnosis of a large group of diseases including cancer. In order to reduce the observer variability and the manual effort involving in ...
    • Smart markers for watershed-based cell segmentation 

      Koyuncu, Can Fahrettin (Bilkent University, 2012)
      Automated cell imaging systems facilitate fast and reliable analysis of biological events at the cellular level. In these systems, the first step is usually cell segmentation that greatly affects the success of the ...
    • Two-tier tissue decomposition for histopathological image representation and classification 

      Gültekin, Tunç (Bilkent University, 2014)
      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 ...