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@@ -51,3 +51,9 @@ deeplabv3plus/figures/dice_distribution.png filter=lfs diff=lfs merge=lfs -text
 deeplabv3plus/figures/dice_iou_distribution.png filter=lfs diff=lfs merge=lfs -text
 deeplabv3plus/figures/metrics_comparison.png filter=lfs diff=lfs merge=lfs -text
 deeplabv3plus/figures/training_curves.png filter=lfs diff=lfs merge=lfs -text
+attention_unet/figures/comparison_visualization.pdf filter=lfs diff=lfs merge=lfs -text
+attention_unet/figures/comparison_visualization.png filter=lfs diff=lfs merge=lfs -text
+attention_unet/figures/dice_distribution.png filter=lfs diff=lfs merge=lfs -text
+attention_unet/figures/dice_iou_distribution.png filter=lfs diff=lfs merge=lfs -text
+attention_unet/figures/metrics_comparison.png filter=lfs diff=lfs merge=lfs -text
+attention_unet/figures/training_curves.png filter=lfs diff=lfs merge=lfs -text

attention_unet/config/experiment_config.json

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+{
+  "timestamp": "20251125_133300",
+  "input_shape": [
+    256,
+    256,
+    1
+  ],
+  "target_size": [
+    256,
+    256
+  ],
+  "epochs": 50,
+  "batch_size": 8,
+  "learning_rate": 0.0001,
+  "validation_split": 0.1,
+  "random_state": 42,
+  "loss_options": {
+    "scenario1": "weighted_bce",
+    "scenario2": "weighted_categorical"
+  }
+}

attention_unet/figures/comparison_visualization.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:971c82da98e8482f223690e5336fda25b5e846f8158acb610427dbb5b611d868
+size 3447300

attention_unet/leverage_summary.txt

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+
+    LEVERAGE PAPER RESULTS SUMMARY
+    ================================
+    Experiment Timestamp: 20251125_133300
+    Model Architecture: ATTN_UNET
+    WMH Segmentation: Binary vs Three-class Classification Comparison
+
+    DATASET INFORMATION:
+    --------------------
+    Training Images: 1044 
+    Test Images: 161
+    Image Size: (256, 256)
+    Classes: Background (0), Normal WMH (1), Abnormal WMH (2)
+
+    METHODOLOGY:
+    ------------
+    Architecture: ATTN_UNET
+    Loss Functions: 
+    - Scenario 1: weighted_bce
+    - Scenario 2: weighted_categorical
+    Training Epochs: 50
+    Batch Size: 8
+    Learning Rate: 0.0001
+
+    PERFORMANCE RESULTS:
+    --------------------
+    OVERLAP-BASED METRICS:
+                            | Scenario 1 (Binary) | Scenario 2 (3-class) | Improvement
+    --------------------|---------------------|----------------------|------------
+    Accuracy            | 0.9844            | 0.9959             | +0.0115
+    Precision           | 0.3236           | 0.7110            | +0.3874
+    Recall              | 0.9769              | 0.7707               | -0.2062
+    Specificity         | 0.9998         | 0.9983          | -0.0016
+    Dice Coefficient    | 0.4861                | 0.7396                 | +0.2535
+    IoU Coefficient     | 0.3211                 | 0.5868                  | +0.2657
+
+    SURFACE-BASED METRICS (lower is better):
+                            | Scenario 1 (Binary) | Scenario 2 (3-class) | Improvement
+    --------------------|---------------------|----------------------|------------
+    HD95 (pixels)       | 52.3479 ± 41.1076 | 47.0514 ± 40.1375 | +5.2965
+    ASSD (pixels)       | 11.1905 ± 12.0022 | 14.1671 ± 18.8798 | -2.9767
+
+    Note: For HD95 and ASSD, positive improvement means reduction (better boundary accuracy)
+    Valid samples: HD95=128/161, ASSD=128/161
+
+    STATISTICAL SIGNIFICANCE:
+    -------------------------
+    DICE COEFFICIENT:
+    Test: Paired t-test
+    t-statistic: 6.1813
+    p-value: 0.0000
+    Effect Size (Cohen's d): 0.4419
+    95% Confidence Interval: [0.0927, 0.1798]
+    Result: SIGNIFICANT improvement
+
+    IoU COEFFICIENT:
+    Test: Paired t-test
+    t-statistic: 6.5713
+    p-value: 0.0000
+    Effect Size (Cohen's d): 0.5197
+    95% Confidence Interval: [0.0961, 0.1786]
+    Result: SIGNIFICANT improvement
+
+    HD95 (95th Percentile Hausdorff Distance):
+    Test: Paired t-test
+    t-statistic: 1.7275
+    p-value: 0.0865
+    Effect Size (Cohen's d): 0.1299
+    95% Confidence Interval: [-0.7706, 11.3635] pixels
+    Result: NOT SIGNIFICANT improvement
+
+    ASSD (Average Symmetric Surface Distance):
+    Test: Paired t-test
+    t-statistic: -2.6433
+    p-value: 0.0092
+    Effect Size (Cohen's d): -0.1874
+    95% Confidence Interval: [-5.2051, -0.7482] pixels
+    Result: SIGNIFICANT improvement
+
+    KEY FINDINGS:
+    -------------
+    OVERLAP-BASED METRICS:
+    1. Three-class segmentation shows 43.87% improvement in Dice coefficient
+    2. Three-class segmentation shows 63.30% improvement in IoU coefficient
+    3. Dice improvement is statistically significant (p<0.05)
+    4. IoU improvement is statistically significant (p<0.05)
+
+    SURFACE-BASED METRICS:
+    5. HD95 shows 10.12% reduction (lower is better)
+    6. ASSD shows 26.60% increase (lower is better)
+    7. HD95 improvement is not statistically significant
+    8. ASSD improvement is statistically significant (p<0.05)
+
+    OVERALL ASSESSMENT:
+    9. Post-processing provided substantial improvements in both scenarios
+    10. Three-class approach shows consistent advantages across multiple metrics
+    11. Boundary accuracy (HD95/ASSD) improved significantly
+
+    FILES GENERATED:
+    ----------------
+    - Models: scenario1_binary_model.h5, scenario2_multiclass_model.h5
+    - Figures: training_curves.png/.pdf, comparison_visualization.png/.pdf, metrics_comparison.png/.pdf
+    - Tables: comprehensive_results.csv/.xlsx, surface_metrics.csv/.xlsx, latex_table.tex, latex_surface_table.tex
+    - Statistics: statistical_analysis.json, statistical_report.txt
+    - Predictions: All test predictions and ground truth data saved
+
+    PUBLICATION READINESS:
+    ----------------------
+    ✓ High-resolution figures (300 DPI, PNG/PDF)
+    ✓ LaTeX-formatted tables (overlap and surface metrics)
+    ✓ Comprehensive statistical analysis (Dice, IoU, HD95, ASSD)
+    ✓ Post-processing impact analysis
+    ✓ Reproducible results with saved models
+    ✓ Professional documentation
+    ✓ Surface-based metrics for boundary accuracy assessment
+    

attention_unet/tables/comprehensive_results.csv

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+Scenario,Accuracy,Precision,Recall,Specificity,Dice,IoU
+Binary Classification (Processed),0.9844384045334336,0.3235688692608949,0.9768807939522773,0.9998217485401418,0.4861214129916778,0.32110992074012756
+Three-class Classification (Processed),0.9959118766073854,0.7110082161258877,0.7706695513263984,0.9982578323964436,0.7396377200887311,0.586845338344574
+Statistical Analysis,Dice p=0.0000,Dice t=6.1813,Dice Δ=0.1363,Dice ES=0.4419,IoU p=0.0000,IoU Δ=0.1373

attention_unet/tables/latex_surface_table.tex

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+\begin{table}
+\caption{Surface-based metrics (HD95 and ASSD) comparison in pixels}
+\label{tab:surface_metrics}
+\begin{tabular}{lrrrrrrll}
+\toprule
+Scenario & HD95_Mean & HD95_Std & HD95_Median & ASSD_Mean & ASSD_Std & ASSD_Median & HD95_Stats & ASSD_Stats \\
+\midrule
+Binary (S1) & 52.3479 & 41.1076 & 45.7810 & 11.1905 & 12.0022 & 6.4209 & NaN & NaN \\
+Three-class (S2) & 47.0514 & 40.1375 & 41.8586 & 14.1671 & 18.8798 & 7.1221 & NaN & NaN \\
+\bottomrule
+\end{tabular}
+\end{table}

attention_unet/tables/latex_table.tex

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+\begin{table}
+\caption{Performance comparison between binary and three-class segmentation approaches}
+\label{tab:performance_comparison}
+\begin{tabular}{lllllll}
+\toprule
+Scenario & Accuracy & Precision & Recall & Specificity & Dice & IoU \\
+\midrule
+Binary Classification (Processed) & 0.9844 & 0.3236 & 0.9769 & 0.9998 & 0.4861 & 0.3211 \\
+Three-class Classification (Processed) & 0.9959 & 0.7110 & 0.7707 & 0.9983 & 0.7396 & 0.5868 \\
+\bottomrule
+\end{tabular}
+\end{table}

attention_unet/tables/surface_metrics.csv

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+Scenario,HD95_Mean,HD95_Std,HD95_Median,ASSD_Mean,ASSD_Std,ASSD_Median,HD95_Stats,ASSD_Stats
+Binary (S1),52.34791573904039,41.10756052285166,45.781031508460934,11.190458976820532,12.002233070337882,6.420893945034037,,
+Three-class (S2),47.05144279949535,40.13751764028808,41.8586480091422,14.167142956119385,18.879824259203055,7.1220741494236055,,
+Statistical Analysis,,,,,,,"Δ=5.2965px, p=0.0865","Δ=-2.9767px, p=0.0092"