29 CNN ( Extraction and Classification of Cell Nuclei Using CNN Features)
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1 29 CNN ( Extraction and Classification of Cell Nuclei Using CNN Features)
2 i Support Vector Machine(SVM) SVM SVM Histograms of Oriented Gradients Convolutional Neural Network AlexNet
3 ii
4 1 1 [1] [2] [3][4] [5] [5] Higher-order Local AutoCorrelation HLAC
5 1 2 [6], Deep Learning Convolutional Neural Network(CNN). Hematoxylin-Eosin HE CNN
6 3 2 [7][8][9] [7][8] 3 2 G [9] [10][11] [10] Genetic Programing GP [12] Simulated Annealing SA [13] Simulated Annealing Programming SAP GP-SAP [10]
7 2 4 [11] Support Vector Machine(SVM) [11] HE HE [11] SVM HE
8 (HE) : HE
9 Support Vector Machine(SVM) SVM, 2 SVM SVM C 1, C : ( ) ( )
10 : SVM,., x 1,, x n, y 1,, y n x i C 1 y i = 1, x i C 2 y i = 1 SVM f(x i ) = sign(g(x i )) g(x i ) = w x i + b (3.1) w b g(x i ) = 0 D(x i ) 0 C 1 g(x i ) < 0 C 2 x +s x s w b
11 3 8 2 (w x +s ) + b = +1 (w x s ) + b = 1 2 w (3.2) x +s x s x i y i (w t x i + b) 1 0 y i (w t x i + b) 1 0 (3.3) L(w) = 1 2 w 2 (3.4) w λ n λ i y i = 0, λ i 0 (i = 1,, n) (3.5) i=1 F (λ i ) = n λ i 1 n λ i λ j y i y j x i x j (3.6) 2 i=1 i,j=1 λ λ g(x i ) = n λ i y i x i x + b (3.7) i s b = y s n λ i y i x i x s (3.8) i s
12 SVM XOR ϕ x i x j ϕ(x i ) ϕ(x j ) SVM SVM 3.3 Histograms of Oriented Gradients Histograms of Oriented Gradients(HOG)[14] HOG HOG : HOG
13 m θ m(x, y) = f x (x, y) 2 + f y (x, y) 2 (3.9) θ = tan 1 f y(x, y) f x (x, y) (3.10) f x (x, y) = I(x + 1, y) I(x 1, y) f y (x, y) = I(x, y + 1) I(x, y 1) (3.11) I(x, y) (x, y) m θ HOG (3 3) =8100
14 Convolutional Neural Network Convolutional Neural Network(CNN) Deep Learning CNN 3.5 CNN CNN 3.5: CNN CNN feed-forward (3.12) a (k) ij = m 1 s=0 n 1 t=0 w (k) s t x (i+s)(j+t) + b (k) (3.12) m n ( ) x k a (k) 2 w (k) b (k) E M, N backpropagation
15 3 12 (3.13) E w (k) s t = M m i=0 N n j=0 E a (k) ij a (k) ij w (k) s t = M m i=0 N n j=0 E a (k) ij x (i+s)(j+t) M m E b = (k) i=0 N n j=0 E a (k) ij a (k) ij b (k) = M m i=0 N n j=0 E a (k) ij (3.13) backpropagation (3.14) δ (k) ij := E a (k) ij (3.14) Rectified Linear Unit( ReLU) ReLU feed-forward ReLU (3.15) a ij = ReLU (x ij ) = max (0, x ij ) (3.15)
16 3 13 backpropagation (3.16) E x ij = E a ij if a ij 0 0 otherwise (3.16) ReLU max pooling mean pooling max pooling feed-forward backpropagation feed-forward (3.17) backpropagation (3.18) a ij = max (x (li+s)(lj+t) ) where s [0, l], t [0, l] (3.17) E x (li+s)(lj+t) = E a ij if a ij = x (li+s)(lj+t) 0 otherwise (3.18) 3.5 AlexNet CNN
![3 14 AlexNet [15] CNN](/docs-images/118/231297825/images/17-0.jpg "ILSVR2012 3.6 3.")
17 3 14 AlexNet [15] CNN ILSVR : AlexNet
18 15 4, SVM 4.1 SVM HE ( ) 400 ( ) HOG CNN
19 4 16 (a) (b) 4.1: 3 CNN Alex-net : HOG& CNN , CNN HOG CNN
20 4 17 CNN CNN HE SVM 4.2 CNN CNN [11] CNN SVM 90px 90px, 5px CNN SVM 2 SVM 4.3
21 : (a) 4.3: (b)
22 b Step 1: Step 2:. Step 3: P P = 2 (S red(i)) (4.1) S red(i) I RGB R RGB R G B 3 4 R RGB R
23 : Step 1: 2. Step 2: 2. Step 3:. Step 4: 2 Step 5: Step
24 4 21 (a) 4.5: (b) HE Step 4 Step 1: Step 2: Step 1 2. Step 3:. Step 4: Step 5: 2 ( )
25
26 HE (a) (b) 5.1:
27 a 5.3a (a) 5.2: (b) (a) 5.3: (b) 5.3 Precision Recall F-measure TP
28 5 25 FP FN True Positive False Positive False Negative T P 80, F P F N. T P P recision = T P + F P T P Recall = T P + F N 2 P recision Recall F measure = P recision + Recall (5.1) (5.2) (5.3) : Precision Recall F-measure F 8 Recall
29 SVM DNA DNA 2 1
30 (a) (b) 6.1: CNN
31 : CNN CNN CNN 6.2 CNN Step 1: Step 2: Step 3: CNN Step 4: SVM Step 5: Step 2 Step CNN 6.2a 6.2b CNN Step 1: x y
32 6 29 (a) CNN (b) CNN 6.2: CNN Step 2:, a b, Step 3: a a Step 3 : a>90 6.3b a a (a) a 90 (b) a>90 6.3:
33 HE 3 HE
34 (a) (b) 7.1: (a) (b) 7.2:
35 7 32 (a) (b) 7.3:
36 : N/C N/C N/C 1 7.2
37 : 7.5:
38 :
39 : NC N/C 7.3 N/C 2 CNN 2 2 ( ) 0 IV 5 IV
40 7 37 ( ) [16][17]
41 38 8 HE CNN SVM HE CNN,
42 39
43 40 [1] Nakhleh, R., Coffin, C., Cooper, K: Recommendations for quality assurance and improvement in surgical and autopsy pathology Hum Pathol, 37, pp , [2] [3] Taylor, C.R., Levenson, R.M: Quantification of immunohistochemistry issues concerning methods, utility and semiquantitative assessment II Histopathology, 49, pp , [4] : The IEICE transactions on information and systems (Japanese edetion) 96(4), pp , 2013 [5], : MPS 2010-MPS-81(32), pp. 1-6, 2010 [6] : Proceedings of the Japan Joint Automatic Control Conference 57(0), pp , 2014 [7],,,,. D-II,, II-. J77-D-2(2), pp , [8],,,: Medical Imaging Technology 14(1), pp , 1996.
44 41 [9],,,,,. MBE, ME. 112(123), pp , [10] : GP SAP MPS 2009-MPS-75(12), pp. 1-6, 2009 [11],,,,, KONICA MINOLTA TECHNOLOGY REPORT. Vol. 13, [12] John R. Koza, Genetic programming, on the programming of computers by means of natural selection MIT Press, 1992 [13] Nicholas Metropolis, Arianna W. Rosenbluth, Marshall N. Rosenbluth, Augusta H. Teller, Edward Teller, Equation of state calculation by fast computing machines The Journal of Chemical Physics 21, pp.1087, 1953 [14] Navneet Dalal, Bill Triggs, Histograms of oriented gradients for human detection, Proc. of IEEE Conference on Computer Vision and Pattern Recognition(CVPR), pp , [15] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks, In Advances in neural information processing systems, pp , [16] Marc Amoyel, Erika A. Bach. Cell competition: how to eliminate your neighbours, Development2014, Development : pp, , 2014 [17] Hogan C., Kajita M., Lawrenson K., Fujita Y. Interactions between normal and transformed epithelial cells: their contributions to tumourigenesis, Int J Biochem Cell Biol 43(4), pp , 2011
45 42 1.,,,,, CNN, 42, Yuya Tsukada, Yuji Iwahori, Kenji Funahashi, Mami Jose, Jun Ueda, Takashi Iwamoto, Extraction of Cell Nuclei using CNN Features, Procedia Computer Science, Elsevier, Vol.112, Pages , 2017.
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