The remaining 95:4% is sampled from non-mitosis pixels by assigning to each pixel p a weight D(p)

We build the actual training set, composed by 1 million instances, which includesĪll mitosis pixels (6:6% of the training instances).

D(p) will be large for challenging non-mitosis pixels. Let D(p) denote the mitosis probability that Cd assigns to pixel p.

We apply Cd to all images in T1 and T2.

Because Cd is trained on a limited training set in which challenging non-mitosis instances are severely underrepresented, it tends to misclassify most non-mitotic nuclei as class mitosis

We use Sd to briefly train a simple DNN classifier Cd.

We build a small training set Sd, which includes all 66000 mitosis instances and the same number of non-mitosis instances, uniformly sampled from the 151 million non-mitosis pixels.

If training instances for class non-mitosis were uniformly sampled from images, most of the training effort would be wasted.Ģ, 去除对训练没有价值的数据，构造更有价值的数据集。其中一种方法是 first detecting all nuclei, then classifying each nucleus separately as mitotic or non-mitotic. 注意到不是所有的 non-mitosis 都有意义。因为如文中所说：In contrast, the largest part of the image area is covered by background pixels far from any nucleus, whose class (non-mitosis) is quite trivial to determine. This results in a total of roughly 66000 mitosis pixels and 151 million non-mitosis pixels。这篇文章发表在 MICCAI2013 ,文章中提出的方法在 ICPR2012 mitosis detection competition 中取得了第一名。其中方法大致为(Fig.1)：ġ,训练一个CNN分类器，判断以每个像素点为中心的正方形框是 mitosis 的概率。ġ, the mitosis class is assigned to all windows whose center pixel is closer than d = 10 pixels to the centroid of a ground-truth mitosis all remaining windows are given the non-mitosis class. 1，Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks

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