docs/inference.md

Inference

Full Pipeline

run_inference.py runs the complete pipeline: preprocessing followed by PI-RADS classification and csPCa risk prediction.

python run_inference.py --config config/config_preprocess.yaml

This script:

1. Runs all four preprocessing steps (register, segment, histogram match, heatmap)
2. Loads the PI-RADS model from models/pirads.pt
3. Loads the csPCa model from models/cspca_model.pth
4. For each scan: predicts PI-RADS score, csPCa risk probability, and identifies the top-5 most-attended patches

Required Model Files

Place these in the models/ directory:

File	Description
pirads.pt	Trained PI-RADS MIL model checkpoint
cspca_model.pth	Trained csPCa model checkpoint
prostate_segmentation_model.pt	Pre-trained prostate segmentation model

Output Format

Results are saved to <output_dir>/results.json:

{
    "patient_001.nrrd": {
        "Predicted PIRAD Score": 4.0,
        "csPCa risk": 0.8234,
        "Top left coordinate of top 5 patches(x,y,z)": [
            [32, 45, 7],
            [28, 50, 7],
            [35, 42, 8],
            [30, 48, 6],
            [33, 44, 8]
        ]
    }
}

Label Mapping

PI-RADS predictions are 0-indexed internally and shifted by +2 for display:

Internal Label	PI-RADS Score
0	PI-RADS 2
1	PI-RADS 3
2	PI-RADS 4
3	PI-RADS 5

csPCa risk is a continuous probability in [0, 1].

Testing Individual Models

PI-RADS Testing

python run_pirads.py --mode test \
    --config config/config_pirads_test.yaml \
    --checkpoint models/pirads.pt

Reports Quadratic Weighted Kappa (QWK) across multiple seeds.

csPCa Testing

python run_cspca.py --mode test \
    --config config/config_cspca_test.yaml \
    --checkpoint_cspca models/cspca_model.pth

Reports AUC, sensitivity, and specificity with 95% confidence intervals across 20 seeds (default).