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@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 trained_models/multiclass_results_20260106_114110_unet/figures/training_curves.png filter=lfs diff=lfs merge=lfs -text
+trained_models/multiclass_results_20260106_121152_attn_unet/figures/training_curves.png filter=lfs diff=lfs merge=lfs -text

trained_models/multiclass_results_20260106_121152_attn_unet/attn_unet_summary.txt

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+
+MULTI-CLASS WMH AND VENTRICLES SEGMENTATION RESULTS
+===================================================
+Experiment Timestamp: 20260106_150912
+Model Architecture: ATTN_UNET
+
+DATASET INFORMATION:
+--------------------
+Training Images: 1775
+Test Images: 199
+Image Size: (256, 256)
+Classes: Background (0), Ventricles (1), Abnormal WMH (2)
+
+METHODOLOGY:
+------------
+Architecture: ATTN_UNET
+Loss Function: weighted_categorical
+Training Epochs: 100
+Batch Size: 8
+Learning Rate: 0.0001
+
+PERFORMANCE RESULTS:
+--------------------
+                            | Ventricles    | Abnormal WMH  | Overall
+--------------------|---------------|---------------|-------------
+Precision           | 0.9117        | 0.8345        | 0.8731
+Recall              | 0.9226           | 0.7670           | 0.8448
+Dice Coefficient    | 0.9171             | 0.7993             | 0.8582
+IoU                 | 0.8469              | 0.6657              | 0.7563
+HD95 (pixels)       | 3.8515             | 20.2336             | 12.0426
+
+KEY FINDINGS:
+-------------
+1. Best performing class: Ventricles
+2. Ventricles Dice: 0.9171
+3. Abnormal WMH Dice: 0.7993
+4. Overall Dice: 0.8582
+
+FILES GENERATED:
+----------------
+- Model: multiclass_model.h5
+- Figures: training_curves.png/.pdf
+- Tables: attn_unet_metrics.csv/.xlsx, attn_unet_latex_table.tex
+- Predictions: All test predictions and ground truth data saved
+
+PUBLICATION READINESS:
+----------------------
+✓ High-resolution figures (300 DPI, PNG/PDF)
+✓ LaTeX-formatted tables
+✓ Comprehensive per-class metrics
+✓ Post-processing applied
+✓ Reproducible results with saved model
+✓ Professional documentation

trained_models/multiclass_results_20260106_121152_attn_unet/config/experiment_config.json

@@ -0,0 +1,20 @@
+{
+  "timestamp": "20260106_121152",
+  "model_name": "attn_unet",
+  "input_shape": [
+    256,
+    256,
+    1
+  ],
+  "target_size": [
+    256,
+    256
+  ],
+  "num_classes": 3,
+  "epochs": 100,
+  "batch_size": 8,
+  "learning_rate": 0.0001,
+  "validation_split": 0.1,
+  "random_state": 42,
+  "loss_function": "weighted_categorical"
+}

trained_models/multiclass_results_20260106_121152_attn_unet/models/multiclass_model.h5

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+size 376915096

trained_models/multiclass_results_20260106_121152_attn_unet/models/training_history.pkl

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trained_models/multiclass_results_20260106_121152_attn_unet/predictions/predictions.npy

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trained_models/multiclass_results_20260106_121152_attn_unet/predictions/test_masks.npy

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trained_models/multiclass_results_20260106_121152_attn_unet/tables/attn_unet_latex_table.tex

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+\begin{table}
+\caption{Performance metrics for attn_unet model}
+\label{tab:attn_unet_metrics}
+\begin{tabular}{lrrrrr}
+\toprule
+Class & Precision & Recall & Dice & IoU & HD95 \\
+\midrule
+Ventricles & 0.9117 & 0.9226 & 0.9171 & 0.8469 & 3.8515 \\
+Abnormal WMH & 0.8345 & 0.7670 & 0.7993 & 0.6657 & 20.2336 \\
+Overall & 0.8731 & 0.8448 & 0.8582 & 0.7563 & 12.0426 \\
+\bottomrule
+\end{tabular}
+\end{table}

trained_models/multiclass_results_20260106_121152_attn_unet/tables/attn_unet_metrics.csv

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+Class,Precision,Recall,Dice,IoU,HD95
+Ventricles,0.911729574773053,0.9225507022166356,0.9171082194917984,0.8469066023826599,3.851465528006527
+Abnormal WMH,0.8344764517604024,0.7669678503887372,0.7992992445052995,0.6656939387321472,20.23364595811516
+Overall,0.8731030132667277,0.8447592763026863,0.8582037319985489,0.7563002705574036,12.042555743060845