SELF-TRAINING       NBIBISSER RAYTCHEV,          PRMU2012-11, Vol.112, No.37, pp.57-62,   ,   (2012 05).
Semi-                        supervised   Proposed    Experimental         Introduction                Algorithm     Setti...
Semi-                     supervised    Proposed    Experimental      Introduction                 Algorithm     Setting  ...
Semi-                                   supervised    Proposed    Experimental                    Introduction            ...
Semi-                          supervised   Proposed    Experimental           Introduction                Algorithm     S...
Semi-                               supervised   Proposed    Experimental                Introduction                Algor...
Proposed          Experimental         Introduction      Self-training   Algorithm           Setting      Result   Conclus...
PROPOSEDALGORITHMAlgorithm 1 •                      Self-training (estimate probability)Algorithm 2 •                     ...
PROPOSEDALGORITHMAlgorithm 1 •                      Self-training (estimate probability)Algorithm 2 •                     ...
Proposed          Experimental                           Introduction    Self-training        Algorithm           Setting ...
Semi-                             supervised   Proposed    Experimental             Introduction                 Algorithm...
PROPOSEDALGORITHMAlgorithm 1 •                      Self-training (estimate probability)Algorithm 2 •                     ...
Proposed          Experimental                        Introduction    Self-training   Algorithm           Setting         ...
PROPOSEDALGORITHMAlgorithm 1 •                      Self-training (estimate probability)Algorithm 2 •                     ...
Proposed          Experimental                                      Introduction      Self-training        Algorithm      ...
EXPERIMENTALSETTING" " " 
Semi-                                 supervised    Proposed    Experimental            Introduction                      ...
Semi-                                 supervised    Proposed    Experimental               Introduction                   ...
Semi-                                 supervised    Proposed    Experimental               Introduction                   ...
Semi-                           supervised   Proposed    Experimental            Introduction                Algorithm    ...
Semi-                           supervised   Proposed    Experimental            Introduction                Algorithm    ...
EXAMPLE OF IMAGES   original image                    labeled sample                       size : 30×30~250×250unlabeled s...
Semi-               supervised   Proposed    ExperimentalIntroduction                Algorithm     Setting      Result   C...
Semi-               supervised   Proposed    ExperimentalIntroduction                Algorithm     Setting      Result   C...
Semi-                             supervised     Proposed            Experimental             Introduction                ...
RESULT"      !      !  C3"      !      ! 
Semi-                                          supervised   Proposed      Experimental                          Introducti...
Semi-                                          supervised       Proposed      Experimental                          Introd...
Semi-                    supervised   Proposed    Experimental     Introduction                Algorithm     Setting      ...
Semi-                     supervised   Proposed    Experimental      Introduction                Algorithm     Setting    ...
CONCLUSION                   Self-training"      ! "      ! FUTURE WORK" "  Self-training" 
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20120527PRMU 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

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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).

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20120527PRMU 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

  1. 1. SELF-TRAINING NBIBISSER RAYTCHEV, PRMU2012-11, Vol.112, No.37, pp.57-62, , (2012 05).
  2. 2. Semi- supervised Proposed Experimental Introduction Algorithm Setting Result Conclusion LearningENDOSCOPICDIAGNOSIS CCD I think this is a cancer… 100
  3. 3. Semi- supervised Proposed Experimental Introduction Algorithm Setting Result Conclusion LearningNARROW-BANDIMAGINGNBI! !  NBI
  4. 4. Semi- supervised Proposed Experimental Introduction Algorithm Setting Result Conclusion LearningNBI 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. 5. Semi- supervised Proposed Experimental Introduction Algorithm Setting Result Conclusion LearningMOTIVATION! !  NBI!  ×  C3 ×  ×  NBI
  6. 6. Semi- supervised Proposed Experimental Introduction Algorithm Setting Result Conclusion Learning ABSTRACTKey Idea :Self-training! !  [ PRMU2011]
  7. 7. Proposed Experimental Introduction Self-training Algorithm Setting Result ConclusionSELF-TRAINING Accept POINT 1.  Reject 2. 
  8. 8. PROPOSEDALGORITHMAlgorithm 1 •  Self-training (estimate probability)Algorithm 2 •  Self-training (estimate probability & estimate label)Algorithm 3 •  Self-training
  9. 9. PROPOSEDALGORITHMAlgorithm 1 •  Self-training (estimate probability)Algorithm 2 •  Self-training (estimate probability & estimate label)Algorithm 3 •  Self-training
  10. 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 jClassifier 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. 11. Semi- supervised Proposed Experimental Introduction Algorithm Setting Result Conclusion LearningORIGINAL LABELCONSTRAINT •  Type A Type B, Type C3 •  Type B Type C3
  12. 12. PROPOSEDALGORITHMAlgorithm 1 •  Self-training (estimate probability)Algorithm 2 •  Self-training (estimate probability & estimate label)Algorithm 3 •  Self-training
  13. 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 jClassifier 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. 14. PROPOSEDALGORITHMAlgorithm 1 •  Self-training (estimate probability)Algorithm 2 •  Self-training (estimate probability & estimate label)Algorithm 3 •  Self-training
  15. 15. Proposed Experimental Introduction Self-training Algorithm Setting Result Conclusion ALGORITHM 3 Self-trainingLabeled 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. 16. EXPERIMENTALSETTING" " " 
  17. 17. Semi- supervised Proposed Experimental Introduction Algorithm Setting Result Conclusion LearningLABELED SAMPLES 100×300 900×800 [pix.] Type A Type B Type C3 Total 359 462 87 908 B C3 A
  18. 18. Semi- supervised Proposed Experimental Introduction Algorithm Setting Result Conclusion LearningUNLABELED SAMPLES 10 30×30 250×250 [pix.]• •  Type A Type B Type C3 Total 3590 4610 870 9070* 10
  19. 19. Semi- supervised Proposed Experimental Introduction Algorithm Setting Result Conclusion LearningUNLABELED SAMPLES 10 30×30 250×250 [pix.]• •  Type A Type B Type C3 Total 3590 4610 870 9070* 10
  20. 20. Semi- supervised Proposed Experimental Introduction Algorithm Setting Result Conclusion Learning widthheight
  21. 21. Semi- supervised Proposed Experimental Introduction Algorithm Setting Result Conclusion Learning widthheight
  22. 22. EXAMPLE OF IMAGES original image labeled sample size : 30×30~250×250unlabeled samples
  23. 23. Semi- supervised Proposed ExperimentalIntroduction Algorithm Setting Result Conclusion Learning
  24. 24. Semi- supervised Proposed ExperimentalIntroduction Algorithm Setting Result Conclusion Learning
  25. 25. Semi- supervised Proposed Experimental Introduction Algorithm Setting Result Conclusion LearningEVALUATION" 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. 26. RESULT"  !  !  C3"  !  ! 
  27. 27. Semi- supervised Proposed Experimental Introduction Algorithm Setting Result Conclusion LearningRESULT 0.96 0.95Recognition Rate 0.94 0.93 0.92 0.91 0.9 Algorithm 1 Algorithm 2 Algorithm 3
  28. 28. Semi- supervised Proposed Experimental Introduction Algorithm Setting Result Conclusion LearningRESULT 0.96 p=0.013314 0.95Recognition Rate 0.94 0.93 0.92 0.91 0.9 Algorithm 1 Algorithm 2 Algorithm 3
  29. 29. Semi- supervised Proposed Experimental Introduction Algorithm Setting Result Conclusion LearningRESULT
  30. 30. Semi- supervised Proposed Experimental Introduction Algorithm Setting Result Conclusion LearningC3 RECALL RATE
  31. 31. CONCLUSION Self-training"  ! "  ! FUTURE WORK" "  Self-training" 

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