卷积神经网络在免疫细胞化学研究中的应用

D. Parpulov, A. Samorodov, D. Makhov, E. Slavnova, N. Volchenko, V. Iglovikov
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引用次数: 7

摘要

乳腺癌的HER2/neu状态对治疗策略的选择很重要,但目前仍由病理学家手工评估。这一程序的自动化是一项紧迫的任务,因为它将使病理学家从日常工作中解脱出来。但由于标本图像中经常存在红细胞背景和非细胞元素,传统的计算机视觉方法难以进行细胞分割。在这项工作中,我们提出了基于深度卷积神经网络的分割算法。已经证明,使用这种方法,可能比使用经典的计算机视觉算法得到更好的结果。
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Convolutional neural network application for cells segmentation in immunocytochemical study
HER2/neu status of breast cancer is important for treatment strategy choice, but nowadays it is evaluated manually by pathologist. The automation of this procedure is an urgent task, because it will allow to free a pathologist from routine work. But the problem of cells segmentation is difficult for classical methods of computer vision due to the frequent presence of erythrocytic background and non-cellular elements at specimen' image. In this work we propose segmentation algorithm, based on deep convolutional neural networks. Is it has been shown that using this approach, it's possible to get better results, than using classical computer vision algorithms.
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