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71512a6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 | # 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`)
---
## 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)
---
## Python package requirements
```bash
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`.
---
## Folder setup
Create this folder and place DICOM files there:
```text
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
└── ...
```
---
## Run commands
From workspace root:
```bash
cd download_imp
python run_inference.py
```
or (same thing):
```bash
python run_interface.py
```
---
## 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.
---
## 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.
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