ğĚġù÷ãÑ6Y7ą�ãð SELF-TRAININGö

:¥�¯ÊNBI��´iüùV��

��>�'ģ�náUģ

BISSER RAYTCHEVģÈ�.hģw�90;ģ

)�\�ģ�p1�ģ¨4/JģAn�Xģ����

ĥNG:Ħ ��

�

Ï<Y7À�? _«7,, ĘĒĤĢ¶¸ģĝĖćĆ�±�� ÕPRMU2012-11, Vol.112, No.37, pp.57-62, *&FHz:?, [�á(2012 05).�

Introduction�Semi-

supervised Learning�

Proposed Algorithm�

Experimental Setting� Result� Conclusion�

ENDOSCOPIC DIAGNOSIS�

Û�

:¥a:�¯Ê´i á��øCCDąbóďċĤęąc�íħ¥�ąyv

��100���øa:íð ¥8¬Ðù{Áæā�ÄOąd��

I think this is a cancer…�

����

�¯Êyv�

¤�ù=3�¶ħfR�

��jÉù%¹�fRìĄð¤�æā ®}ÿ�ÄOąd@�

Introduction�Semi-

supervised Learning�

Proposed Algorithm�

Experimental Setting� Result� Conclusion�

NARROW-BAND IMAGING�NBI�� ! �K6ù�ąQôăëöõ©�Ñ6ąP·¬�

! °BìĄă©�{Áö�ù�ÄOçÍÂ�

ÀL°B� NBI°B� Ü�

NBI CLASSIFICATION�!  Stehle et al., ’09 :á©�Ñ6ùđĊĝĢĕĤĎĞĢ�!  Gross et al., ’09 :á©�{Áą`�

!  Tamaki ACCV2010, )��PRMU2011 :áBag-of-Visual WordsøĀ㶸�

Introduction�Semi-

supervised Learning�

Proposed Algorithm�

Experimental Setting� Result� Conclusion�

hyperplasia (HP)�

tubular adenoma(TA)�

M~SM-s�

SM-s�

Type A�

Type B�

Type C3�

�ùÃÄO�

~L�

§W¤��

�

)��PRMU2011�

¶¸Cº�

Stehle et al. Gross et al. �

NBIa:]­�ÓÕ[H. Kanao et al., ‘09]�

�

MOTIVATION���¶¸ù�O+�

! :®}÷?¢ĖĤĒđĔė

9�9|÷o�ùNBI��ą¶¸îăðþù :®}÷ĖĤĒĚĤďù{��

Introduction�Semi-

supervised Learning�

Proposed Algorithm�

Experimental Setting� Result� Conclusion�

!  NBI��ĖĤĒđĔėù�»

! :Çù��øğĚġé ×  ċďė ×  ^Ì ×  DË�¸

Þ�

A�B�C3�

ABSTRACT�

Self-training ! ğĚġù÷ãÑ6øCíôğĚġé

! ¶¸�ą�êă?¢ĖĤĒđĔėą{��

Introduction�Semi-

supervised Learning�

Proposed Algorithm�

Experimental Setting� Result� Conclusion�

Sr^�Õ[)��PRMU2011]� ex^��

Key Idea : ğĚġù÷ãÑ6Y7ą�ãă

�

SELF-TRAINING�ğĚġ÷íĖĤĒą�ãôW£ą+� ?¢ĖĤĒą¦#�\

�?¢�

m?¢�

¶¸�

ğĚġèĖĤĒ�

ğĚġ÷íĖĤĒ�

�ÒWyv�Accept�

Reject�

POINT 1.  ğĚġù�äj

2.  ¿"îăčĢęġùÆûj

Introduction� Self-training� Proposed Algorithm�

Experimental Setting� Result� Conclusion�

à�

PROPOSED ALGORITHM�

Algorithm 1

• �ÒWyvøĀăSelf-training (estimate probability)

Algorithm 2

• �ÒWyvøĀăSelf-training (estimate probability &

estimate label)

Algorithm 3

• �TĚĉėġù¼Î!@øĀăSelf-training

Ø×�

PROPOSED ALGORITHM�

Algorithm 1

• �ÒWyvøĀăSelf-training (estimate probability)

Algorithm 2

• �ÒWyvøĀăSelf-training (estimate probability &

estimate label)

Algorithm 3

• �TĚĉėġù¼Î!@øĀăSelf-training

ØØ�

Unlabeled samples�

ALGORITHM 1��ÒWyvøĀăSelf-training (estimate probability)

Introduction� Self-training� Proposed Algorithm�

Experimental Setting� Result� Conclusion�

L ULabeled samples�

fClassifier� A 0.5�

B 0.7�

A 0.9�

B 0.9�

A 0.9�

C3 0.6�

9.0=≥ THEPj

jEPjEL

f B 0.9�

A 0.8�

B 0.5�EP

EL

Estimate label�

Estimate probability�

9.0=≥ THEPj

ØÙ�

Introduction�Semi-

supervised Learning�

Proposed Algorithm�

Experimental Setting� Result� Conclusion�

ORIGINAL LABEL CONSTRAINT����øúêOùÔãğĚġç�äāĄôãă

•  Type A ù���øúType B, Type C3ú=3í÷ã •  Type B ù���øúType C3ú=3í÷ã

ØÚ�

PROPOSED ALGORITHM�

Algorithm 1

• �ÒWyvøĀăSelf-training (estimate probability)

Algorithm 2

• �ÒWyvøĀăSelf-training (estimate probability &

estimate label)

Algorithm 3

• �TĚĉėġù¼Î!@øĀăSelf-training

ØÛ�

Unlabeled samples�

ALGORITHM 2��ÒWyvøĀăSelf-training (estimate probability & estimate label)

Introduction� Self-training� Proposed Algorithm�

Experimental Setting� Result� Conclusion�

L ULabeled samples�

fClassifier� A 0.5�

B 0.7�

A 0.9�

B 0.9�

A 0.9�

C3 0.6�

9.0=≥ THEPj

jEPjEL

f B 0.9�

A 0.8�

B 0.5�EP

EL

Estimate label�

Estimate probability�

jj ELy =

Original labels� ulljjy

++= 1}{

B A A B B C3

A B C3

jj ELy =9.0=≥ THEPj

B

B 0.8�

ØÜ�

PROPOSED ALGORITHM�

Algorithm 1

• �ÒWyvøĀăSelf-training (estimate probability)

Algorithm 2

• �ÒWyvøĀăSelf-training (estimate probability &

estimate label)

Algorithm 3

• �TĚĉėġù¼Î!@øĀăSelf-training

ØÝ�

Unlabeled samples�

ALGORITHM 3��TĚĉėġù¼Î!@øĀăSelf-training

Introduction� Self-training� Proposed Algorithm�

Experimental Setting� Result� Conclusion�

L ULabeled samples�

B 0.9�

A 0.7�

A 1.9�

C3 1.5�

A 2.9�

A 2.6�

Labels�liiy 1}{ =

),d( ji xx iy

5.1),d(min <ji xx),d(min ji xx

fClassifier�

∑=

−=128

1

2)(),d(d

jdidji xxxx

ØÞ�

EXPERIMENTAL SETTING�

" ğĚġèčĢęġ

" ğĚġ÷íčĢęġ

" µ�j�

Øß�

LABELED SAMPLES�DË$øĀòôėĠěĢĊģğĚĠĢĊ ��čĈĐĨ100×300ĩ900×800 [pix.]

��tgù�³

�

Type A� Type B� Type C3� Total�359� 462� 87� 908�

Introduction�Semi-

supervised Learning�

Proposed Algorithm�

Experimental Setting� Result� Conclusion�

Øà�

A�B� C3�

UNLABELED SAMPLES �(���æā10tïó�Ă�í ��čĈĐĨ30×30ĩ250×250 [pix.]

�Ă�íj • ���æāğĢēĜø�Ă�í • ğĚġèÑ6ù-¾æā�Ă�í

��tgù�³

*ğĚġèčĢęġù��óñé���ç=3í÷ãðþâğĚġ÷íčĢęġç10tE÷ã

Introduction�Semi-

supervised Learning�

Proposed Algorithm�

Experimental Setting� Result� Conclusion�

Type A� Type B� Type C3� Total�3590� 4610� 870� 9070�

���

Ù×�

UNLABELED SAMPLES �(���æā10tïó�Ă�í ��čĈĐĨ30×30ĩ250×250 [pix.]

�Ă�íj • ���æāğĢēĜø�Ă�í • ğĚġèÑ6ù-¾æā�Ă�í

��tgù�³

*ğĚġèčĢęġù��óñé���ç=3í÷ãðþâğĚġ÷íčĢęġç10tE÷ã

Introduction�Semi-

supervised Learning�

Proposed Algorithm�

Experimental Setting� Result� Conclusion�

Type A� Type B� Type C3� Total�3590� 4610� 870� 9070�

���

ÙØ�

Introduction�Semi-

supervised Learning�

Proposed Algorithm�

Experimental Setting� Result� Conclusion�

���æāğĢēĜø�Ă�îÕ�

9�9|÷čĢęġ�\�

height�

width�

ÙÙ�

Introduction�Semi-

supervised Learning�

Proposed Algorithm�

Experimental Setting� Result� Conclusion�

ğĚġùãðÑ6-¾ą�Ă�î�

?¢čĢęġøÓ�íðčĢęġ�\

height�

width�

ÙÚ�

original image labeled sample

unlabeled samples

EXAMPLE OF IMAGES�

ÙÛ�

size : 30×30~250×250�

ğĢēĜ÷Ñ6Y7�

Introduction�Semi-

supervised Learning�

Proposed Algorithm�

Experimental Setting� Result� Conclusion�

ÙÜ�

ğĚġèÑ6-¾Y7�

Introduction�Semi-

supervised Learning�

Proposed Algorithm�

Experimental Setting� Result� Conclusion�

ÙÝ�

EVALUATION�" 10 hold out testingøĀòô102ùM5�ą�þă " ty@øĀĂlZIą�þă

Introduction�Semi-

supervised Learning�

Proposed Algorithm�

Experimental Setting� Result� Conclusion�

~��ĥ¶¸�Ħ�

C3ù����

Estimated Category�

Type A� Type B� Type C3�True

Category�Type A� (1)� (2)� (3)�Type B� (4)� (5)� (6)�

Type C3� (7)� (8)� (9)�

)9()8()7()6()5()4()3()2()1()9()5()1(

++++++++

++=

)9()8()7()9(++

=

ÙÞ�

RESULT�

" Sr^�ĥğĚġèčĢęġùý��Ħöù¶¸W£ù�½

! ¶¸� !  C3���ù�½�

" ğĚġ÷íčĢęġøĀ㶸�ù�½�

! ğĢēĜø�Ă�íðğĚġ÷íĖĤĒą�ãð¡u ! ğĚġèÑ6ù-¾ą�Ă�íðĖĤĒą�ãð¡u

�

Ùß�

RESULT�

0.9

0.91

0.92

0.93

0.94

0.95

0.96

Sr^�� Algorithm 1 Algorithm 2 Algorithm 3

Rec

ogni

tion

Rat

e�

ğĢēĜø�Ă�íðčĢęġą���

Introduction�Semi-

supervised Learning�

Proposed Algorithm�

Experimental Setting� Result� Conclusion�

Ùà��TÇ

Ěĉėġù¼Î d@�� ùý��

d@�� d@ğĚġ

RESULT�

0.9

0.91

0.92

0.93

0.94

0.95

0.96

Sr^�� Algorithm 1 Algorithm 2 Algorithm 3

Rec

ogni

tion

Rat

e �

ğĚġèÑ6-¾ą�Ă�íðčĢęġą���

ÖÕp=0.013314�

Introduction�Semi-

supervised Learning�

Proposed Algorithm�

Experimental Setting� Result� Conclusion�

Ú×��TÇ

Ěĉėġù¼Î d@�� ùý��

d@�� d@ğĚġ

RESULT�

Introduction�Semi-

supervised Learning�

Proposed Algorithm�

Experimental Setting� Result� Conclusion�

ÚØ�

ğĢēĜ -¾�

�TÇ Ěĉėġù¼Î

d@�� ùý��

d@�� d@ğĚġ

C3 RECALL RATE�

Introduction�Semi-

supervised Learning�

Proposed Algorithm�

Experimental Setting� Result� Conclusion�

ÚÙ�

ğĢēĜ -¾�

�TÇ Ěĉėġù¼Î

d@�� ùý��

d@�� d@ğĚġ

CONCLUSION�¶¸W£ą+�îăðþù:®}÷ĖĤĒđĔė�\

ğĚġù÷ãÑ6Y7ą�ãăSelf-trainingĆġČĠĐĜ

"  Sr^�öù¶¸�ù�½

!  ex^�øĀ㶸W£+� "  ğĚġ÷íčĢęġøĀ㶸�ù�½

!  ğĚġèÑ6-¾ùY7ą�ãăëöõ¶¸W£çĀĂ+� FUTURE WORK

"  ¿"ìĄăğĚġ÷íčĢęġù±s "  Self-trainingøåéăkÅ÷ �q�ù²@ "  ù¶¸^�üùV� Ú

Ú�