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    A whole-slide image grading benchmark and tissue classification for cervical cancer precursor lesions with inter-observer variability
    (Springer Science and Business Media Deutschland GmbH, 2021) Albayrak, Abdulkadir; Ünlü Akhan, Aslı; Çalık, Nurullah; Çapar, Abdulkerim; Bilgin, Gökhan; Töreyin, Behçet Uğur; Müezzinoğlu, Bahar; Türkmen, İlknur; Durak Ata, Lütfiye
    he cervical cancer developing from the precancerous lesions caused by the human papillomavirus (HPV) has been one of the preventable cancers with the help of periodic screening. Cervical intraepithelial neoplasia (CIN) and squamous intraepithelial lesion (SIL) are two types of grading conventions widely accepted by pathologists. On the other hand, inter-observer variability is an important issue for final diagnosis. In this paper, a whole-slide image grading benchmark for cervical cancer precursor lesions is created and the “Uterine Cervical Cancer Database” introduced in this article is the first publicly available cervical tissue microscopy image dataset. In addition, a morphological feature representing the angle between the basal membrane (BM) and the major axis of each nucleus in the tissue is proposed. The presence of papillae of the cervical epithelium and overlapping cell problems are also discussed. Besides that, the inter-observer variability is also evaluated by thorough comparisons among decisions of pathologists, as well as the final diagnosis. [Figure not available: see fulltext.].
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    Classification of cervical precursor lesions via local histogram and cell morphometric features
    (IEEE-Institute of Electrical and Electronics Engineers Inc., 2023) Çalık, Nurullah; Albayrak, Abdulkadir; Akhan, Aslı; Türkmen, İlknur; Çapar, Abdülkerim; Töreyin, Behçet Uğur; Bilgin, Gökhan; Müezzinoğlu, Bahar; Durak Ata, Lütfiye
    Cervical squamous intra-epithelial lesions (SIL) are precursor cancer lesions and their diagnosis is important because patients have a chance to be cured before cancer develops. In the diagnosis of the disease, pathologists decide by considering the cell distribution from the basal to the upper membrane. The idea, inspired by the pathologists' point of view, is based on the fact that cell amounts differ in the basal, central, and upper regions of tissue according to the level of Cervical Intraepithelial Neoplasia (CIN). Therefore, histogram information can be used for tissue classification so that the model can be explainable. In this study, two different classification schemes are proposed to show that the local histogram is a useful feature for the classification of cervical tissues. The first classifier is Kullback Leibler divergence-based, and the second one is the classification of the histogram by combining the embedding feature vector from morphometric features. These algorithms have been tested on a public dataset.The method we propose in the study achieved an accuracy performance of 78.69% in a data set where morphology-based methods were 69.07% and Convolutional Neural Network (CNN) patch-based algorithms were 75.77%. The proposed statistical features are robust for tackling real-life problems as they operate independently of the lesions manifold.
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    Rahim ağzı (serviks) kanserinde öncü lezyonların evrişimsel sinir aglarıyla bölütlenmesi
    (IEEE, 2017) Albayrak, Abdulkadir; Ünlü, Aslı; Çalık, Nurullah; Bilgin, Gökhan; Türkmen, İlknur; Çakır, Aslı; Çapar, Abdulkerim; Töreyin, Behçet Uğur; Durak Ata, Lütfiye
    Ülkemizde ve dünyada en sık görülen kanser tiplerinden olan rahim ağzı (serviks) kanseri, kanser öncüsü lezyonlarından gelişmektedir. Kanser öncüsü bu lezyonların saptanması, hastanın kanser olmadan tedavi olmasına olanak sağladığiçin önemlidir ve analizleri yapan patologlar tarafından tanısı konmaktadır. Bu çalışmada evrişimsel sinir ağları (ESA) yöntemi kullanılarak kanser öncüsü lezyonların otomatik tespitini gerçekletiren bir sistem tasarlanmıştır. Eğitim aşamasında sistemin görüntülerden lezyonları tanıma başarımı %92 olarak elde edilmektedir. Eğitim aşamasından sonra bütün görüntüler 60×60 boyutlarında bir pencere ile evriştirilerek bölütlenmektedir. İlgili lezyonların Dice katsayısına göre %81.71 başarı ile bölütlendiği bir model oluşturulmuştur.