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Mixture of learners for cancer stem cell detection using CD13 and H and e stained images

: Oguz, O.; Akbas, C.E.; Mallah, M.; Tagdemir, K.; Akhan Güzelcan, E.; Muenzenmayer, C.; Wittenberg, T.; Üner, A.; Cetin, A.E.; Atalay, R.C.


Gurcan, M.N. ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.:
Medical Imaging 2016. Digital Pathology : 2-3 March 2016, San Diego, California
Bellingham, WA: SPIE, 2016 (Proceedings of SPIE 9791)
ISBN: 9781510600263
Art. 97910Y
Conference "Medical Imaging - Digital Pathology" <2016, San Diego/Calif.>
Conference Paper
Fraunhofer IIS ()

In this article, algorithms for cancer stem cell (CSC) detection in liver cancer tissue images are developed. Conventionally, a pathologist examines of cancer cell morphologies under microscope. Computer aided diagnosis systems (CAD) aims to help pathologists in this tedious and repetitive work. The first algorithm locates CSCs in CD13 stained liver tissue images. The method has also an online learning algorithm to improve the accuracy of detection. The second family of algorithms classify the cancer tissues stained with H and E which is clinically routine and cost effective than immunohistochemistry (IHC) procedure. The algorithms utilize 1D-SIFT and Eigen-Analysis based feature sets as descriptors. Normal and cancerous tissues can be classified with 92.1% accuracy in H and E stained images. Classification accuracy of low and high-grade cancerous tissue images is 70.4%.