Now showing items 1-11 of 11

    • Altçizge modellemesi kullanarak kolon bez tespiti 

      Özgül, Etkin Barış; Sökmensüer, C.; Gündüz-Demir, Çiğdem (IEEE, 2011-04)
      Kolon adenokarsinomu, kolon bez yapılarında değişimlere yol açar. Patologlar bezlerdeki bu değişimleri değerlendirerek kolon adenokarsinom tanı ve derecelendirmesi yaparlar. Ancak değişimlerin değerlendirme süreci kaydadeğer ...
    • AttentionBoost: learning what to attend for gland segmentation in histopathological images by boosting fully convolutional networks 

      Güneşli, Gözde Nur; Sökmensüer, C.; Gündüz-Demir, Çiğdem (IEEE, 2020)
      Fully convolutional networks (FCNs) are widely used for instance segmentation. One important challenge is to sufficiently train these networks to yield good generalizations for hard-to-learn pixels, correct prediction of ...
    • Canlı hücre bölütlemesi için gözeticili öğrenme modeli 

      Koyuncu, Can Fahrettin; Durmaz, İrem; Çetin-Atalay, Rengül; Gündüz-Demir, Çiğdem (IEEE Computer Society, 2014-04)
      Automated cell imaging systems have been proposed for faster and more reliable analysis of biological events at the cellular level. The first step of these systems is usually cell segmentation whose success affects the ...
    • Gauss tabanlı modelleme kullanarak canlı hücre görüntülerinin öğreticisiz bölütlenmesi 

      Arslan, Salim; Durmaz, İrem; Çetin-Atalay, Rengül; Gündüz-Demir, Çiğdem (2011-04)
      The first step of targeted cancer drug development is to screen and determine drug candidates by in vitro measuring the effectiveness of the drugs. The tests developed for this purpose can be time consuming due to their ...
    • 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 ...
    • Kanser tanısı için kolon bezlerinin matematiksel analizi 

      Çığır, Celal; Sökmensüer, C.; Gündüz-Demir, Çiğdem (IEEE, 2009-04)
      Neoplastic diseases including cancer cause organizational changes in tissues. Histopathological examination, which is routinely used for the diagnosis and grading of these diseases, relies on pathologists to identify such ...
    • Kelime histogram modeli ile histopatolojik görüntü sınıflandırılması 

      Özdemir, Erdem; Sökmensüer, C.; Gündüz-Demir, Çiğdem (IEEE, 2011-04)
      Colon cancer, which is one of the most common cancer type, could be cured with its early diagnosis. In the current practice of medicine, there are many screening techniques such as colonoscopy, sigmoidoscopy, and stool ...
    • Test-cost sensitive classification based on conditioned loss functions 

      Cebe, Mümin; Gündüz-Demir, Çiğdem (Springer, 2007-09)
      We report a novel approach for designing test-cost sensitive classifiers that consider the misclassification cost together with the cost of feature extraction utilizing the consistency behavior for the first time. In this ...
    • Tissue object patterns for segmentation in histopathological images 

      Gündüz-Demir, Çiğdem (ACM, 2011)
      In the current practice of medicine, histopathological examination is the gold standard for routine clinical diagnosis and grading of cancer. However, as this examination involves the visual analysis of biopsies, it is ...
    • Unsupervised feature extraction via deep learning for histopathological classification of colon tissue images 

      Sarı, Can Taylan; Gündüz-Demir, Çiğdem (Institute of Electrical and Electronics Engineers, 2019)
      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. ...
    • Unsupervised tissue image segmentation through object-oriented texture 

      Tosun, Akif Burak; Sokmensuer, C.; Gündüz-Demir, Çiğdem (IEEE, 2010)
      This paper presents a new algorithm for the unsupervised segmentation of tissue images. It relies on using the spatial information of cytological tissue components. As opposed to the previous study, it does not only use ...