INFERENCE_USAGE.md

Improved Model Inference — Usage Guide (download_imp)

What this runner does

run_inference.py runs the latest improved model trained from the notebooks in improvement/:

• EfficientNet-B4 backbone (tf_efficientnet_b4)
• 2.5D input (prev + center + next slices → 9 channels)
• 6 outputs (any + 5 hemorrhage subtypes)
• 5-fold ensemble (best_model_fold0..4.pth)
• Saved calibration (isotonic/temperature) from calibration_params.json

Outputs:

• Per-slice JSON report (outputs/reports/*.json)
• Slice-level CSV (outputs/slice_predictions.csv)
• Patient-level CSV (outputs/patient_predictions.csv)

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Required files (already in download_imp/)

• best_model_fold0.pth
• best_model_fold1.pth
• best_model_fold2.pth
• best_model_fold3.pth
• best_model_fold4.pth
• calibration_params.json
• isotonic_models.pkl
• normalization_stats.json
• manifest.csv (optional at inference time; used only for true_any if IDs match)

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Python package requirements

pip install -r requirements.txt

Notes:

• timm is required for tf_efficientnet_b4 model construction.
• scikit-learn is needed to deserialize and use isotonic_models.pkl.

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Folder setup

Create this folder and place DICOM files there:

download_imp/
├── run_inference.py
├── run_interface.py
├── best_model_fold0.pth
├── best_model_fold1.pth
├── best_model_fold2.pth
├── best_model_fold3.pth
├── best_model_fold4.pth
├── calibration_params.json
├── isotonic_models.pkl
├── normalization_stats.json
├── manifest.csv
└── dicom_inputs/
    ├── ID_xxx1.dcm
    ├── ID_xxx2.dcm
    └── ...

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Run commands

From workspace root:

cd download_imp
python run_inference.py

or (same thing):

python run_interface.py

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Important behavior

• No CLI arguments; all settings are at top of run_inference.py (CONFIG section).
• FOLD_SELECTION controls checkpoint selection:
  • "ensemble" = use all available folds and average logits
  • 0..4 = use one specific fold only
• If best_method is isotonic, the runner uses isotonic_models.pkl.
• Missing prev/next slice in a series is handled exactly like training cache logic: neighbor falls back to center slice.
• Decision threshold defaults to threshold_at_spec90 from calibration_params.json unless overridden in config.

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Recommended production checklist

1. Keep all fold checkpoints and calibration files in the same download_imp/ directory.
2. Verify DICOMs are non-contrast head CT slices before inference.
3. Run once on a small sample and review slice_predictions.csv and JSON reports.
4. Have radiologist review all flagged and uncertain cases.