Now showing items 1-7 of 7

    • Color graphs for automated cancer diagnosis and grading 

      Altunbay, D.; Cigir, C.; Sokmensuer, C.; Gunduz Demir, C. (Institute of Electrical and Electronics Engineers, 2010-03)
      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 ...
    • Graph walks for classification of histopathological images 

      Olgun, Gülden; Sokmensuer, C.; Gündüz-Demir, Çiğdem (IEEE, 2013)
      This 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 hybrid classification model for digital pathology using structural and statistical pattern recognition 

      Ozdemir, E.; Gunduz-Demir, C. (Institute of Electrical and Electronics Engineers, 2013)
      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 ...
    • A resampling-based Markovian model for automated colon cancer diagnosis 

      Ozdemir, E.; Sokmensuer, C.; Gunduz Demir, C. (Institute of Electrical and Electronics Engineers, 2012-01)
      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 ...
    • 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 ...
    • Two-tier tissue decomposition for histopathological image representation and classification 

      Gultekin, T.; Koyuncu, C. F.; Sokmensuer, C.; Gunduz Demir, C. (Institute of Electrical and Electronics Engineers, 2015)
      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 ...
    • Unsupervised feature extraction via deep learning for histopathological classification of colon tissue images 

      Sari, C. T.; Gunduz Demir, C. (Institute of Electrical and Electronics Engineers, 2018)
      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. ...