Informatics Institute faculty member Behçet Uğur Töreyin, Lütfiye Durak-Ata and Abdulkerim Çapar coauthored paper titled 'A whole-slide image grading benchmark and tissue classification for cervical cancer precursor lesions with inter-observer variability' has been published in 'Medical & Biological Engineering & Computing' in 2021. 

DOI: https://doi.org/10.1007/s11517-021-02388-w

Cite:
Albayrak, A., Akhan, A.U., Calik, N., Capar, A., Bilgin, G., Toreyin, B.U., Muezzinoglu, B., Turkmen, I. and Durak-Ata, L., 2021. A whole-slide image grading benchmark and tissue classification for cervical cancer precursor lesions with inter-observer variability. Medical & Biological Engineering & Computing, 59(7), pp.1545-1561.

Abstract: 
The 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