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Treatment Outcome Prediction Model of Visual Field Recovery Using SOM. JOJO 2011.12.22. Outline. Basic knowledge Treatment Outcome Prediction Model Feature selection Self-organizing-maps Conclusion. Outline. Basic knowledge Treatment Outcome Prediction Model Feature selection - PowerPoint PPT Presentation
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Treatment Outcome Prediction Model of Visual Field Recovery Using SOM
JOJO
2011.12.22
Outline
Basic knowledge
Treatment Outcome Prediction Model
• Feature selection
• Self-organizing-maps
Conclusion
Outline
Basic knowledge
Treatment Outcome Prediction Model
• Feature selection
• Self-organizing-maps
Conclusion
Basic knowledge
1 Diagnosis of damage to the visual system
High Resolution Perimetry(HRP)
Reaction time
Detection
Basic knowledge
1 Diagnosis of damage to the visual systemDiagnostic spots definition:
Basic knowledge
2 Vision Restoration Training(VRT)After damages to visual system, spontaneous
recovery happens.When the recovery finished, VRT is used to treat
patients.
How can we know the results of VRT before it’s
applied?
Basic knowledge
3 Treatment Outcome PredictionStep1: build a TOPM with patients’ baseline diagnosis
and diagnostic chartsStep2: extract features from a patient’s baseline
diagnosis chartStep3: predict the treatment outcome with TOPM
Outline
Basic knowledge
Treatment Outcome Prediction Model
• Feature selection
• Self-organizing-maps
Conclusion
TOMP (FS)
• Equ • L
• l• g Size of
Residual and defect
areaReaction
TimeConformity
to hemianopia
and quadrantan
opia
Border Diffuseness
Global
features
TOMP (FS)Conformity to hemianopia and quadrantanopia
TOMP (FS)
• Eccentricity(离心率 )• L
• l• g Distance to
Scotoma Neighborhood
measures
Visual field positionResidual
Function
Local featur
es
TOMP (SOM)1 Theory: Winner takes all
TOMP (SOM)
Local featur
e
TOMP (SOM)2 Prediction: the winner takes all decided
TOMP (SOM)3 Results:
TOMP (SOM)3 Results: (Model evaluation: 10-fold cross validation)
P: the number of hot spotsN: the number of cold spotsTP: correctly classified positive samplesFP: incorrectly classified positive samples
TOMP (SOM)
ROC:
3 Results: (Model evaluation: 10-fold cross validation)
TOMP (SOM)3 Model evaluation: 10-fold cross validation
TPR FPR ACC AUC
SOM 0.81
SVM 0.83
PCA 0.92
44%±4.7%
45.3%±4.5% 86.8%±1.1%3.2%±0.8%
84.2%±1.4%6%±1.9%
4.7%±1.0%68.5%±4.0% 90.0%±0.8%
Outline
Basic knowledge
Treatment Outcome Prediction Model
• Feature selection
• Self-organizing-maps
Conclusion
Conclusion
Why choose SOM?
• Its non-linearity and self-organization methodology
allows a comprehensible adaptation to the data
distribution.
• Simplify the process of data mining and the feature
selection phase by conveniently combining both
prediction and data exploration.
Thank you!