Now showing items 1-5 of 5

    • Deep learning for digital pathology 

      Sarı, Can Taylan (Bilkent University, 2020-11)
      Histopathological 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 ...
    • DeepDistance: a multi-task deep regression model for cell detection in inverted microscopy images 

      Koyuncu, Can Fahrettin; Güneşli, Gözde Nur; Çetin-Atalay, Rengül; Gündüz-Demir, Çigdem (Elsevier, 2020)
      This paper presents a new deep regression model, which we call DeepDistance, for cell detection in images acquired with inverted microscopy. This model considers cell detection as a task of finding most probable locations ...
    • Mining web images for concept learning 

      Golge, Eren (Bilkent University, 2014-08)
      We attack the problem of learning concepts automatically from noisy Web image search results. The idea is based on discovering common characteristics shared among category images by posing two novel methods that are able ...
    • Prototypes : exemplar based video representation 

      Yalçınkaya, Özge (Bilkent University, 2016-06)
      Recognition of actions from videos is a widely studied problem and there have been many solutions introduced over the years. Labeling of the training data that is required for classification has been an important bottleneck ...
    • 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. ...