竹田孝碑史, 玉木徹, ライチェフ ビゼル, 金田和文, 栗田多喜夫, 吉田成人, 竹村嘉人, 蔭地啓市, 宮木理恵, 田中信治： 「ラベルのない領域情報を用いたSelf-training と 大腸内視鏡NBI 画像診断への応用」, 電子情報通信学会技術報告, パターン認識・メディア理解研究会 PRMU2012-11, Vol.112, No.37, pp.57-62, 名古屋工業大学, 愛知　(2012 05).

1. SELF-TRAINING
NBI
BISSER RAYTCHEV
, PRMU2012-11, Vol.112, No.37, pp.57-62, , (2012 05).

2. Semi-
supervised Proposed Experimental
Introduction Algorithm Setting Result Conclusion
Learning
ENDOSCOPIC
DIAGNOSIS
CCD
I think this is a cancer…
100

3. Semi-
supervised Proposed Experimental
Introduction Algorithm Setting Result Conclusion
Learning
NARROW-BAND
IMAGING
NBI
!
!
NBI

4. Semi-
supervised Proposed Experimental
Introduction Algorithm Setting Result Conclusion
Learning
NBI CLASSIFICATION
! Stehle et al., ’09 :
! Gross et al., ’09 :
! Tamaki ACCV2010, PRMU2011 : Bag-of-Visual
Words
NBI [H. Kanao et al., ‘09]
hyperplasia (HP) Type A
Stehle et al.
tubular adenoma(TA) Gross et al. Type B
PRMU2011
M~SM-s
SM-s Type C3

5. Semi-
supervised Proposed Experimental
Introduction Algorithm Setting Result Conclusion
Learning
MOTIVATION
!
! NBI
!
×
C3
×
×
NBI

6. Semi-
supervised Proposed Experimental
Introduction Algorithm Setting Result Conclusion
Learning
ABSTRACT
Key Idea :
Self-training
!
!
[ PRMU2011]

7. Proposed Experimental
Introduction Self-training Algorithm Setting Result Conclusion
SELF-TRAINING
Accept POINT
1.
Reject
2.

8. PROPOSED
ALGORITHM
Algorithm 1
• Self-training (estimate probability)
Algorithm 2
• Self-training (estimate probability &
estimate label)
Algorithm 3
• Self-training

9. PROPOSED
ALGORITHM
Algorithm 1
• Self-training (estimate probability)
Algorithm 2
• Self-training (estimate probability &
estimate label)
Algorithm 3
• Self-training

10. Proposed Experimental
Introduction Self-training Algorithm Setting Result Conclusion
ALGORITHM 1
Self-training (estimate probability)
Labeled samples L Unlabeled samples U
Estimate label EL j
Classifier f A B A B A C3
Estimate probability EPj
0.5 0.7 0.9 0.9 0.9 0.6
EPj ≥ TH = 0.9
EL
f B A B
EP
0.9 0.8 0.5
EPj ≥ TH = 0.9

11. Semi-
supervised Proposed Experimental
Introduction Algorithm Setting Result Conclusion
Learning
ORIGINAL LABEL
CONSTRAINT
• Type A Type B, Type C3
• Type B Type C3

12. PROPOSED
ALGORITHM
Algorithm 1
• Self-training (estimate probability)
Algorithm 2
• Self-training (estimate probability &
estimate label)
Algorithm 3
• Self-training

13. Proposed Experimental
Introduction Self-training Algorithm Setting Result Conclusion
ALGORITHM 2
Self-training (estimate probability & estimate label)
l +u
Labeled samples L Unlabeled samples U Original labels{ y j } j =l +1
B A A B B C3
Estimate label EL j
Classifier f A B A B A C3
Estimate probability EPj
0.5 0.7 0.9 0.9 0.9 0.6
EPj ≥ TH = 0.9 y j = EL j
B A B C3
EL
f B A B B
EP
0.9 0.8 0.8 0.5
EPj ≥ TH = 0.9 y j = EL j

14. PROPOSED
ALGORITHM
Algorithm 1
• Self-training (estimate probability)
Algorithm 2
• Self-training (estimate probability &
estimate label)
Algorithm 3
• Self-training

15. Proposed Experimental
Introduction Self-training Algorithm Setting Result Conclusion
ALGORITHM 3
Self-training
Labeled samples L
l
Labels { yi }i =1 Unlabeled samples U
d( x i , x j ) yi B A A C3 A A
128 min d( xi , x j ) 0.9 0.7 1.9 1.5 2.9 2.6
d( x i , x j ) = ∑ (x id − x jd ) 2 min d( xi , x j ) < 1.5
d =1
Classifier f

16. EXPERIMENTAL
SETTING
"
"
"

17. Semi-
supervised Proposed Experimental
Introduction Algorithm Setting Result Conclusion
Learning
LABELED SAMPLES
100×300 900×800 [pix.]
Type A Type B Type C3 Total
359 462 87 908
B C3
A

18. Semi-
supervised Proposed Experimental
Introduction Algorithm Setting Result Conclusion
Learning
UNLABELED SAMPLES
10
30×30 250×250 [pix.]
•
•
Type A Type B Type C3 Total
3590 4610 870 9070
* 10

19. Semi-
supervised Proposed Experimental
Introduction Algorithm Setting Result Conclusion
Learning
UNLABELED SAMPLES
10
30×30 250×250 [pix.]
•
•
Type A Type B Type C3 Total
3590 4610 870 9070
* 10

20. Semi-
supervised Proposed Experimental
Introduction Algorithm Setting Result Conclusion
Learning
width
height

21. Semi-
supervised Proposed Experimental
Introduction Algorithm Setting Result Conclusion
Learning
width
height

22. EXAMPLE OF IMAGES
original image
labeled sample
size : 30×30~250×250
unlabeled samples

23. Semi-
supervised Proposed Experimental
Introduction Algorithm Setting Result Conclusion
Learning

24. Semi-
supervised Proposed Experimental
Introduction Algorithm Setting Result Conclusion
Learning

25. Semi-
supervised Proposed Experimental
Introduction Algorithm Setting Result Conclusion
Learning
EVALUATION
" 10 hold out testing 10
" t
(1) + (5) + (9)
=
(1) + (2) + (3) + (4) + (5) + (6) + (7) + (8) + (9)
(9)
C3 =
(7) + (8) + (9)
Estimated Category
Type A Type B Type C3
True Type A (1) (2) (3)
Category Type B (4) (5) (6)
Type C3 (7) (8) (9)

26. RESULT
"
!
! C3
"
!
!

27. Semi-
supervised Proposed Experimental
Introduction Algorithm Setting Result Conclusion
Learning
RESULT
0.96
0.95
Recognition Rate
0.94
0.93
0.92
0.91
0.9
Algorithm 1 Algorithm 2 Algorithm 3

28. Semi-
supervised Proposed Experimental
Introduction Algorithm Setting Result Conclusion
Learning
RESULT
0.96
p=0.013314
0.95
Recognition Rate
0.94
0.93
0.92
0.91
0.9
Algorithm 1 Algorithm 2 Algorithm 3

29. Semi-
supervised Proposed Experimental
Introduction Algorithm Setting Result Conclusion
Learning
RESULT

30. Semi-
supervised Proposed Experimental
Introduction Algorithm Setting Result Conclusion
Learning
C3 RECALL RATE

31. CONCLUSION
Self-training
"
!
"
!
FUTURE WORK
"
" Self-training
"