Mitotic counts are widely used as a metric for cellular proliferation for prognosis and to determine the aggressiveness of individual cancers. This study presents a less labor-intensive method to count mitotic cells in breast cell sections. The proposed algorithm involves two phases: candidate segmentation and detection. During candidate segmentation, images are filtered through a blue ratio threshold to remove unnecessary background information and to increase the color difference between targets and non-targets for an entire digitized image. A fuzzy candidate segmentation method is used to adaptively determine threshold values in order to dichotomize gray-level images and distinguish the images of mitotic candidates from the background. The thresholding scheme integrates the spatial characteristics’ distribution in a histogram to determine an intensity threshold for the processed image, in order to filter insignificant information. During the detection phase, a two-class classification uses an attention mechanism that is realized by a set of fully connected neural networks, instead of convolutional layers, which decreases the computational cost. The validation test using ICPR2012 competition datasets shows that the proposed model outperforms current state-of-art techniques, in terms of the metrics, Accuracy, F₁-score, and Precision and Recall.

中文翻译：

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学习有丝分裂分类的模糊分割方法

有丝分裂计数被广泛用作细胞增殖的量度以用于预后并确定单个癌症的侵袭性。这项研究提出了一种较少劳动强度的方法来计数乳腺细胞切片中的有丝分裂细胞。所提出的算法涉及两个阶段：候选分割和检测。在候选分割过程中，通过蓝比率阈值对图像进行过滤，以删除不必要的背景信息，并增加整个数字化图像的目标和非目标之间的色差。模糊候选者分割方法用于自适应确定阈值，以便将灰度图像分为两部分，并从背景中区分有丝分裂候选者的图像。阈值方案将空间特征的分布整合到直方图中，以确定处理后图像的强度阈值，以过滤不重要的信息。在检测阶段，两类分类使用注意力机制，该机制由一组完全连接的神经网络实现，而不是卷积层，从而降低了计算成本。使用ICPR2012竞争数据集进行的验证测试表明，在度量标准Accuracy，F方面，所提出的模型优于当前的最新技术 这降低了计算成本。使用ICPR2012竞争数据集进行的验证测试表明，在度量标准Accuracy，F方面，所提出的模型优于当前的最新技术 这降低了计算成本。使用ICPR2012竞争数据集进行的验证测试表明，在度量标准Accuracy，F方面，所提出的模型优于当前的最新技术₁分，以及精确度和召回率。