The proliferative activity of breast tumors, which is routinely estimated by
counting of mitotic figures in hematoxylin and eosin stained histology
sections, is considered to be one of the most important prognostic markers.
However, mitosis counting is laborious, subjective and may suffer from low
inter-observer agreement. With the wider acceptance of whole slide images in
pathology labs, automatic image analysis has been proposed as a potential
solution for these issues. In this paper, the results from the Assessment of
Mitosis Detection Algorithms 2013 (AMIDA13) challenge are described. The
challenge was based on a data set consisting of 12 training and 11 testing
subjects, with more than one thousand annotated mitotic figures by multiple
observers. Short descriptions and results from the evaluation of eleven methods
are presented. The top performing method has an error rate that is comparable
to the inter-observer agreement among pathologists.

本研究介绍了自动图像分析在乳腺肿瘤组织分析中的应用，通过分析 AMIDA13 数据集和 11 种不同的算法，提出了一种性能可与病理学家间交互的标准相媲美的图像分析算法。