{
    "archive_metadata": {
        "archive_name": "Attention-Gated 3D U-Net Final Evaluation Reference",
        "created_utc": "2026-09-06T00:34:32.152419+00:00",
        "evaluation_block": "Block 13",
        "archive_block": "Block 14",
        "status": "FINAL_ATTENTION_REFERENCE"
    },
    "repository": {
        "huggingface_repo": "Reduanul1997/RepositoryAttention3DUNet",
        "repo_type": "model"
    },
    "model": {
        "name": "Attention3DUNet",
        "architecture": "Attention-Gated 3D U-Net",
        "input_channels": 4,
        "output_classes": 4,
        "parameters": 5669856
    },
    "dataset": {
        "dataset": "BraTS2020",
        "modalities": [
            "FLAIR",
            "T1",
            "T1ce",
            "T2"
        ],
        "classes": [
            "Background",
            "NCR/NET",
            "Edema",
            "Enhancing Tumor"
        ],
        "training_patients": 294,
        "validation_patients": 74,
        "patch_size": [
            64,
            64,
            64
        ]
    },
    "model_selection": {
        "best_model_epoch": 34,
        "best_validation_dice_from_training": 0.764262,
        "note": "This is the validation Dice reported by the training pipeline and used for model selection. It is distinct from the independently recomputed full-validation confusion-matrix metrics."
    },
    "evaluation_reference": {
        "foreground_macro": {
            "dice": 0.724903,
            "iou": 0.578467,
            "precision": 0.76502,
            "recall": 0.6984
        },
        "tumor_binary": {
            "dice": 0.911371,
            "iou": 0.837173,
            "precision": 0.926928,
            "recall": 0.896327,
            "sensitivity": 0.896327,
            "false_negative_rate": 0.103673,
            "false_positive_rate": 0.021416,
            "TP": 4044236,
            "TN": 14567831,
            "FP": 318815,
            "FN": 467774
        },
        "per_class": {
            "Unknown": {
                "Model": "Attention3DUNet",
                "Best_Model_Epoch": 34.0,
                "Class_ID": 3.0,
                "Class_Name": "Enhancing Tumor",
                "Dice": 0.8116382728647753,
                "IoU": 0.68298924000303,
                "Precision": 0.8477082780883921,
                "Recall": 0.7785125474484258,
                "Sensitivity": 0.7785125474484258,
                "FNR": 0.2214874525515647,
                "FPR": 0.0081125672772594,
                "TP": 827966.0,
                "TN": 18186388.0,
                "FP": 148745.0,
                "FN": 235557.0
            }
        }
    },
    "baseline_comparison_policy": {
        "primary_segmentation_metric": "Foreground Macro Dice",
        "secondary_segmentation_metrics": [
            "Foreground Macro IoU",
            "Foreground Macro Precision",
            "Foreground Macro Recall"
        ],
        "primary_fn_metric": "Tumor False Negative Rate (FNR)",
        "additional_fn_metric": "Tumor False Negatives (FN)",
        "comparison_rule": "Future models must be evaluated using the same validation split, preprocessing, class definitions, tumor-vs-background definition, and metric calculation procedure before direct comparison with this model."
    },
    "baseline_vs_attention_comparison": {
        "baseline_model": {
            "name": "Baseline 3D U-Net",
            "repository": "Reduanul1997/RepositoryBaseLine3DuNet",
            "parameters": 22580964,
            "best_epoch": 45,
            "best_training_dice": 0.772125,
            "foreground_macro_dice": 0.757824,
            "foreground_macro_iou": 0.618727,
            "foreground_macro_precision": 0.781685,
            "foreground_macro_recall": 0.740964,
            "tumor_dice": 0.927715,
            "tumor_iou": 0.865175,
            "tumor_precision": 0.942361,
            "tumor_recall": 0.913516,
            "tumor_fnr": 0.086484,
            "tumor_fpr": 0.017968,
            "tumor_tp": 4312069,
            "tumor_tn": 14414613,
            "tumor_fp": 263746,
            "tumor_fn": 408228
        },
        "attention_model": {
            "name": "Attention-Gated 3D U-Net",
            "repository": "Reduanul1997/RepositoryAttention3DUNet",
            "parameters": 5669856,
            "best_epoch": 34,
            "best_training_dice": 0.764262,
            "foreground_macro_dice": 0.724903,
            "foreground_macro_iou": 0.578467,
            "foreground_macro_precision": 0.76502,
            "foreground_macro_recall": 0.6984,
            "tumor_dice": 0.911371,
            "tumor_iou": 0.837173,
            "tumor_precision": 0.926928,
            "tumor_recall": 0.896327,
            "tumor_fnr": 0.103673,
            "tumor_fpr": 0.021416,
            "tumor_tp": 4044236,
            "tumor_tn": 14567831,
            "tumor_fp": 318815,
            "tumor_fn": 467774
        },
        "interpretation": {
            "parameter_reduction_percent": 74.89099225347509,
            "foreground_macro_dice_difference": -0.03292100000000009,
            "tumor_fnr_difference": 0.017188999999999996,
            "tumor_fn_difference": 59546,
            "best_foreground_macro_dice": "Baseline 3D U-Net",
            "best_tumor_fnr": "Baseline 3D U-Net",
            "most_parameter_efficient": "Attention-Gated 3D U-Net",
            "conclusion": "The Attention-Gated 3D U-Net is substantially more parameter-efficient, but the Baseline 3D U-Net achieves better segmentation and lower tumor false-negative performance on the current validation set."
        }
    },
    "artifact_files": [
        "attention_evaluation_results.json",
        "attention_evaluation_metrics.csv",
        "attention_per_class_metrics.csv",
        "attention_multiclass_confusion_matrix.png",
        "attention_tumor_background_confusion_matrix.png",
        "attention_final_evaluation_summary.json",
        "baseline_vs_attention_comparison.json",
        "baseline_vs_attention_comparison.csv",
        "attention_final_evaluation_package.zip"
    ]
}