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Classification of Cervical Precursor Lesions via Local Histogram and Cell Morphometric Features., , , , , , , , and . IEEE J. Biomed. Health Informatics, 27 (4): 1747-1757 (April 2023)Automated scoring of CerbB2/HER2 receptors using histogram based analysis of immunohistochemistry breast cancer tissue images., , , , and . Biomed. Signal Process. Control., (2021)Segmentation of precursor lesions in cervical cancer using convolutional neural networks., , , , , , , , and . SIU, page 1-4. IEEE, (2017)Gradient-based shape descriptors., , and . Mach. Vis. Appl., 20 (6): 365-378 (2009)Object segmentation and recognition using gradient based descriptors and shape driven fast marching methods (Gradyan temelli betimleyiciler ve şekil güdümlü hızlı yürüme tekniğiyle nesne bölütleme ve sınıflandırma). İstanbul Technical University, Turkey, (2010)A whole-slide image grading benchmark and tissue classification for cervical cancer precursor lesions with inter-observer variability., , , , , , , , and . Medical Biol. Eng. Comput., 59 (7): 1545-1561 (2021)Concurrent Segmentation and Recognition with Shape-Driven Fast Marching Methods., and . ICPR (1), page 155-158. IEEE Computer Society, (2006)A multi-level thresholding based segmentation method for microscopic fluorescence in situ hybridization (FISH) images., , , , , , and . SIU, page 849-852. IEEE, (2016)Shape Recognition by Voting on Fast Marching Iterations., and . ACIVS, volume 5807 of Lecture Notes in Computer Science, page 379-388. Springer, (2009)A Whole Slide Image Grading Benchmark and Tissue Classification for Cervical Cancer Precursor Lesions with Inter-Observer Variability., , , , , , , , and . CoRR, (2018)