Aloraidi Nada A, Sirinukunwattana Korsuk, Khan Adnan M, Rajpoot Nasir M
Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:3370-3. doi: 10.1109/EMBC.2014.6944345.
Mitotic activity is one of the main criteria that pathologists use to decide the grade of the cancer. Computerised mitotic cell detection promises to bring efficiency and accuracy into the grading process. However, detection and classification of mitotic cells in breast cancer histopathology images is a challenging task because of the large intra-class variation in the visual appearance of mitotic cells in various stages of cell division life cycle. In this paper, we test the hypothesis that cells in histopathology images can be effectively represented using cell exemplars derived from sub-images of various kinds of cells in an image for the purposes of mitotic cell classification. We compare three methods for generating exemplar cells. The methods have been evaluated in terms of classification performance on the MITOS dataset. The experimental results demonstrate that eigencells combined with support vector machines produce reasonably high detection accuracy among all the methods.
有丝分裂活性是病理学家用于判定癌症分级的主要标准之一。计算机化的有丝分裂细胞检测有望为分级过程带来效率和准确性。然而，由于在细胞分裂生命周期的各个阶段，有丝分裂细胞的视觉外观存在较大的类内差异，因此在乳腺癌组织病理学图像中检测和分类有丝分裂细胞是一项具有挑战性的任务。在本文中，我们检验了这样一个假设：为了进行有丝分裂细胞分类，可以使用从图像中各种细胞的子图像派生的细胞样本有效地表示组织病理学图像中的细胞。我们比较了三种生成样本细胞的方法。这些方法已根据在MITOS数据集上的分类性能进行了评估。实验结果表明，在所有方法中，特征细胞与支持向量机相结合可产生相当高的检测准确率。
