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@@ -35,3 +35,4 @@ saved_model/**/* 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_125323_trans_unet/figures/training_curves.png filter=lfs diff=lfs merge=lfs -text

trained_models/multiclass_results_20260106_125323_trans_unet/config/experiment_config.json

@@ -0,0 +1,20 @@
+{
+  "timestamp": "20260106_125323",
+  "model_name": "trans_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_125323_trans_unet/models/multiclass_model.h5

@@ -0,0 +1,3 @@
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trained_models/multiclass_results_20260106_125323_trans_unet/models/training_history.pkl

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trained_models/multiclass_results_20260106_125323_trans_unet/predictions/predictions.npy

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trained_models/multiclass_results_20260106_125323_trans_unet/predictions/test_masks.npy

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trained_models/multiclass_results_20260106_125323_trans_unet/tables/trans_unet_latex_table.tex

@@ -0,0 +1,13 @@
+\begin{table}
+\caption{Performance metrics for trans_unet model}
+\label{tab:trans_unet_metrics}
+\begin{tabular}{lrrrrr}
+\toprule
+Class & Precision & Recall & Dice & IoU & HD95 \\
+\midrule
+Ventricles & 0.9133 & 0.8948 & 0.9039 & 0.8247 & 8.8822 \\
+Abnormal WMH & 0.8810 & 0.6412 & 0.7422 & 0.5901 & 24.8415 \\
+Overall & 0.8972 & 0.7680 & 0.8231 & 0.7074 & 16.8619 \\
+\bottomrule
+\end{tabular}
+\end{table}

trained_models/multiclass_results_20260106_125323_trans_unet/tables/trans_unet_metrics.csv

@@ -0,0 +1,4 @@
+Class,Precision,Recall,Dice,IoU,HD95
+Ventricles,0.9132987910189982,0.8947762817568711,0.9039426610837258,0.8247220516204834,8.88221087063197
+Abnormal WMH,0.8810394610202117,0.6411711143797717,0.7422061864133375,0.5900857448577881,24.841499838840388
+Overall,0.897169126019605,0.7679736980683214,0.8230744237485317,0.7074038982391357,16.86185535473618

trained_models/multiclass_results_20260106_125323_trans_unet/trans_unet_summary.txt

@@ -0,0 +1,53 @@
+
+MULTI-CLASS WMH AND VENTRICLES SEGMENTATION RESULTS
+===================================================
+Experiment Timestamp: 20260106_153758
+Model Architecture: TRANS_UNET
+
+DATASET INFORMATION:
+--------------------
+Training Images: 1775
+Test Images: 199
+Image Size: (256, 256)
+Classes: Background (0), Ventricles (1), Abnormal WMH (2)
+
+METHODOLOGY:
+------------
+Architecture: TRANS_UNET
+Loss Function: weighted_categorical
+Training Epochs: 100
+Batch Size: 8
+Learning Rate: 0.0001
+
+PERFORMANCE RESULTS:
+--------------------
+                            | Ventricles    | Abnormal WMH  | Overall
+--------------------|---------------|---------------|-------------
+Precision           | 0.9133        | 0.8810        | 0.8972
+Recall              | 0.8948           | 0.6412           | 0.7680
+Dice Coefficient    | 0.9039             | 0.7422             | 0.8231
+IoU                 | 0.8247              | 0.5901              | 0.7074
+HD95 (pixels)       | 8.8822             | 24.8415             | 16.8619
+
+KEY FINDINGS:
+-------------
+1. Best performing class: Ventricles
+2. Ventricles Dice: 0.9039
+3. Abnormal WMH Dice: 0.7422
+4. Overall Dice: 0.8231
+
+FILES GENERATED:
+----------------
+- Model: multiclass_model.h5
+- Figures: training_curves.png/.pdf
+- Tables: trans_unet_metrics.csv/.xlsx, trans_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