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 6 C r e a t i n g a s y s t e m f r o m b a c k u p. 2 V i e w i n g s y s t e m p r o p e r t i e s. 1 C o n n e c t i n g a P C t o t h e c o n t r o l l e r. 1 1 L o a d i n g a n d s a v i n g p r o g r a m s a n d m o d u l e s. 1 0 P r o g r a m m i n g e x t e r n a l a x e s. 4 T u n i n g t h e m o t i o n b e h a v i o r. 2 S e t t i n g u p t h e M u l t i M o v e. 1 A b o u t p r o g r a m m i n g M u l t i M o v e. 9 P r o g r a m m i n g M u l t i M o v e s y s t e m s. 8 T e s t i n g p o s i t i o n s a n d m o t i o n s. 1 W o r k f l o w f o r p r o g r a m m i n g a r o b o t. 2 T r o u b l e s h o o t i n g a n d o p t i m i z i n g g e o m e t r i e s. 1 I m p o r t i n g a s t a t i o n c o m p o n e n t. 2 T r a c k m o t i o n o f t y p e I R B T x 0 0 4. 1 T r a c k m o t i o n o f t y p e R T T o r I R B T x 0 0 3. 90 2.4 Manually setting up system based on RobotWare 5.xx with track motion. 87 2.3 Creating a system with external axes automatically. 1 S e t t i n g C o n v e y o r t r a c k i n g.
2 S e t t i n g t h e C o n v e y o r t r a c k i n g s t a t i o n. 1 W o r k f l o w o f b u i l d i n g a s t a t i o n. 1 6 A t t a c h i n g a n d d e t a c h i n g o b j e c t s. 1 0 T h e C o n t r o l l e r S t a t u s w i n d o w. 5 T h e C o n t r o l l e r b r o w s e r. 3 T h e P a t h s & T a r g e t s b r o w s e r. 1 R i b b o n, t a b s a n d g r o u p s. 2 A c t i v a t i n g R o b o t S t u d i o. 1 I n s t a l l a t i o n o p t i o n s a n d p r e r e q u i s i t e s. 3 I n s t a l l i n g a n d l i c e n s i n g R o b o t S t u d i o. 8 L i b r a r i e s, g e o m e t r i e s a n d C A D f i l e s. 7 R o b o t a x i s c o n f i g u r a t i o n s. 4 C o n c e p t s o f p r o g r a m m i n g.