Nucleus Detection in Cervical Samples Stained With AgNOR

João Gustavo Atkinson Amorim, Vinícius Moreno Sanches, Tainee Bottamedi, André Victória Matias, Marco Antônio Martins Cavaco, Alexandre Sherlley Onofre, Fabiana B Botelho Onofre, A. von Wangenheim
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Abstract

ABSTRACTCervical cancer is a public health problem, where the treatment hasa better chance of success if detected early. This paper explores oneway of to analyze argyrophilic nucleolus organizer regions (AgNOR)stained slide using deep learning approaches of object detection fordetecting the different categories of nucleus. Our results show thata balanced dataset between the explored categories was essential,also that a ResNet-50 as backbone of Fast RCNN shows an AP of61.8% and 42.5% to detect nucleus and out of focus nucleus.
AgNOR染色宫颈核检测
宫颈癌是一个公共卫生问题,如果发现得早,治疗成功的机会就会更大。本文探讨了一种利用目标检测的深度学习方法来分析嗜银核仁组织区(AgNOR)染色玻片的方法,以检测不同类别的核。结果表明,在不同分类之间建立平衡的数据集是至关重要的,并且以ResNet-50为主干的Fast RCNN检测核和失焦核的AP分别为61.8%和42.5%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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