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Update unet/leverage_summary.txt

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  LEVERAGE PAPER RESULTS SUMMARY
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  ================================
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  Experiment Timestamp: 20251124_152044
 
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  WMH Segmentation: Binary vs Three-class Classification Comparison
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  DATASET INFORMATION:
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  METHODOLOGY:
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  ------------
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- Architecture: Enhanced U-Net with Batch Normalization and Dropout
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  Loss Functions:
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  - Scenario 1: weighted_bce
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  - Scenario 2: weighted_categorical
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  3. Dice analysis confirms significant improvement
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  4. IoU analysis confirms significant improvement
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  5. Post-processing provided substantial improvements in both scenarios
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-
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- FILES GENERATED:
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- ----------------
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- - Models: scenario1_binary_model.h5, scenario2_multiclass_model.h5
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- - Figures: training_curves.png/.pdf, comparison_visualization.png/.pdf, metrics_comparison.png/.pdf
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- - Tables: comprehensive_results.csv/.xlsx, latex_table.tex
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- - Statistics: statistical_analysis.json, statistical_report.txt
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- - Predictions: All test predictions and ground truth data saved
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-
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- PUBLICATION READINESS:
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- ----------------------
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- ✓ High-resolution figures (300 DPI, PNG/PDF)
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- ✓ LaTeX-formatted tables
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- ✓ Comprehensive statistical analysis (Dice + IoU)
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- ✓ Post-processing impact analysis
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- ✓ Reproducible results with saved models
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- ✓ Professional documentation
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-
 
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  LEVERAGE PAPER RESULTS SUMMARY
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  ================================
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  Experiment Timestamp: 20251124_152044
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+ Model Architecture: UNET
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  WMH Segmentation: Binary vs Three-class Classification Comparison
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  DATASET INFORMATION:
 
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  METHODOLOGY:
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  ------------
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+ Architecture: UNET
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  Loss Functions:
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  - Scenario 1: weighted_bce
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  - Scenario 2: weighted_categorical
 
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  3. Dice analysis confirms significant improvement
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  4. IoU analysis confirms significant improvement
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  5. Post-processing provided substantial improvements in both scenarios