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M A C H I N E L E A R N I N G
D I A G N O S E C A N C E R
H O W T O
W I T H
A N D S H O W T H A T I T I S C O O L A N D A C C E S S I B L E
T O A N Y D E V E L O P E R ~ S I M O N VA N D Y K

Diagnosing Cancer with Machine Learning

C h a r l e s
B a b b a g e
L E G E N D

A D A
L O V E L A C E
L E G E N D

M O D E L SW H aT
A r e ?
C o m p u t e r
D e s k t o p
s e r v e r
w i n d o w s
C l i p b o a r d
F i l e f o l d e r

A r t i f i c i a l
N e u r a l
N e t w o r k
T h e

C o n s i d e r
t h e n e u r o n
~ 1 0 b i l l i o n
E x c i t e d / i n h i b i t e d
e l a s t i c

“a p p r o x i m at e t h e
g e n e r a l i s at i o n o f
k n o w l e d g e a n d d i s c o v e r y —
t h e y l e a r n ”

A . t u r i n G o n
l e a r n i n g

“ I n s t e a d o f t r y i n g t o p r o d u c e a p r o g r a m t o
s i m u l at e t h e a d u lt m i n d , w h y n o t r at h e r
t r y t o p r o d u c e o n e w h i c h s i m u l at e s t h e
c h i l d ’ s . I f t h i s w e r e t h e n s u b j e c t e d t o a n
a p p r o p r i at e c o u r s e o f e d u c at i o n o n e w o u l d
o b ta i n t h e a d u lt m i n d . ” ~ A . T u r i n g

p e r c e p t r o n
f ( n e t )
n e t = 0 . 1 * 0 . 9 + 0 . 7 * 0 . 4 + 1 . 3 * 0 . 6
0 . 7
0 . 1
1 . 3
I n p u t s
0 . 9
0 . 4
0 . 6
W e i g h t s
0 . 8 5 o u t p u t
?

p e r c e p t r o n
f ( n e t )
n e t = v 1 * w 1 + v 2 * w 2 + v 3 * w 3
v 2
v 1
v 3
I n p u t s
w 1
w 2
w 3
W e i g h t s
o u t
o u t p u t

o u t = F ( n e t )
n e t = v 1 * w 1 + v 2 * w 2 + v 3 * w 3
SIgmoid
0 . 0
0 . 5
1 . 0
0 . 8 5

o u t = F ( n e t )
STEP
0 . 0
0 . 5
1 . 0
0 . 8 5

e x a m p l e : o r
v1 v2 target
0 0 0
0 1 1
1 0 1
1 1 1
v 1
v 2
w 1
w 2
o u t
g u e s s t h e
w e i g h t s

e x a m p l e : o r
v1 v2 target
0 0 0
0 1 1
1 0 1
1 1 1
0
0
0
0
n e t = 0
n e t = v 1 .w 1
+ v 2 .w 2
( w 1 )
( w 2 )
0
w e ’ r e
g o o d !

v1 v2 target
0 0 0
0 1 1
1 0 1
1 1 1
e x a m p l e : o r
0
1
0
0
n e t = v 1 .w 1
+ v 2 .w 2
( w 1 )
( w 2 )
0
n e t = 0

v1 v2 target
0 0 0
0 1 1
1 0 1
1 1 1
e x a m p l e : o r
0
1
0
1
n e t = v 1 .w 1
+ v 2 .w 2
( w 1 )
( w 2 )
1
n e t = 1
w e ’ r e
g o o d !

v1 v2 target
0 0 0
0 1 1
1 0 1
1 1 1
e x a m p l e : o r
1
0
0
1
n e t = v 1 .w 1
+ v 2 .w 2
( w 1 )
( w 2 )
0
n e t = 0

v1 v2 target
0 0 0
0 1 1
1 0 1
1 1 1
e x a m p l e : o r
1
0
1
1
n e t = v 1 .w 1
+ v 2 .w 2
( w 1 )
( w 2 )
1
n e t = 1
w e ’ r e
g o o d !

v1 v2 target
0 0 0
0 1 1
1 0 1
1 1 1
e x a m p l e : o r
1
1
1
1
n e t = v 1 .w 1
+ v 2 .w 2
w e ’ r e
g o o d !
( w 1 )
( w 2 )
1
n e t = 2

v1 v2 target
0 0 0
0 1 1
1 0 1
1 1 1
o r x o r
v1 v2 target
0 0 0
0 1 1
1 0 1
1 1 0
0
0
0
0
1 1
1 1

C o m p o s e t h e m
v 1
v 2
v 3
P e r c e p t r o n s
i n p u t s
o u t p u t
f
f
f

d i a g n o s i n g
c a n c e r
f o r r e a l s

cell radius … texture DIAGNOsis
1.23 … 4.56 Malignant
… … … …
0.41 … 2.3 Benign
c l a s s i f i c at i o n d ata
J a m e s
S a r a h
j e f f
at t r i b u t e s

A t t r i b u t e s a r e c o m p u t e d
f r o m a d i g i t i z e d i m a g e o f
a f i n e n e e d l e a s p i r at e
( F N A ) o f a b r e a s t m a s s .

t r a i n i n g
17.99 10.38 … 1.78 M
ta r g e to u t p u t
M
1.34 0.8 … 1.8 B B
2.7 4.o2 … 2.5 M B
6.52 1.33 … 5.91 B B
7 5 %

e va l u at i o n
M
o u t p u t
1.34 0.8 … 1.8
t r a i n e d
n e t w o r k
u n s e e n d ata

s e a r c h
g r a d i e n t d e s c e n t
r p r o p
s i m u l at e d a n n e a l i n g
g e n e t i c a l g o r i t h m
d i f f e r e n t i a l e v o l u t i o n
p a r t i c l e s w a r m o p t i m i z e r
c o e v o l u t i o n
a n t s y s t e m
q p r o p
m o n t e c a r l o

$ d e m o
h t t p s : // i n t e l l i g e n c e - r u b y f u z a 2 0 1 5 . h e r o k u a p p. c o m /

c a n c e ri s
d e s t r u c t i v e

b u td i a g n o s i s
i s s o lva b l e

s t o c h a s t i c
m a c h i n e s
t h e r i s e o f

“ I f a m a c h i n e i s e x p e c t e d
t o b e i n f a l l i b l e , i t c a n n o t
a l s o b e i n t e l l i g e n t ”
— A . T u r i n g

! @ s i e f i
s i g h m i n / d i a g n o s i n g - c a n c e r - w i t h - a i
s i m o n @ p l at f o r m 4 5 . c o m
"
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q u e s t i o n s

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Diagnosing Cancer with Machine Learning

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