| # Cohort-Aware CT Reproduction |
|
|
| This repository implements the first-stage reproduction scaffold for |
| `paper.pdf`: CT-first pulmonary lesion characterization with optional PET, |
| clinical missingness, segmentation support maps, calibration hooks and |
| discrete-time survival output. |
|
|
| ## Current Scope |
|
|
| - Implemented: LIDC/Lung-PET-CT-Dx/NSCLC manifest loaders, DICOM/NIfTI/NPZ |
| preprocessing hooks, 3D support network, CT/PET encoders, selective PET gate, |
| clinical encoder, survival head, losses, metrics, training and evaluation CLI. |
| - Reserved: NLST schema and survival interface only. The raw NLST data is not |
| downloaded in this phase. |
| - Deferred: exact TCIA RTSTRUCT/XML parsing details and pathology regularization |
| until the corresponding data is present locally. |
|
|
| ## Environment |
|
|
| Use the requested conda environment: |
|
|
| ```bash |
| conda activate ct |
| python -m pip install -r requirements-project.txt |
| ``` |
|
|
| ## Smoke Test |
|
|
| ```bash |
| conda run -n ct python scripts/create_synthetic_manifest.py --out-dir processed/synthetic --n 8 |
| conda run -n ct pytest -q |
| ``` |
|
|
| ## Training Entry Points |
|
|
| ```bash |
| conda run -n ct python -m src.train --config configs/lidc_seg.yaml |
| conda run -n ct python -m src.train --config configs/lung_pet_ct_dx.yaml |
| conda run -n ct python -m src.train --config configs/nsclc_external.yaml |
| ``` |
|
|
| Manifests must point to preprocessed lesion crops. Each row should include |
| `ct_path`; optional columns are `pet_path`, `mask_path`, `label`, `time`, |
| `event`, `patient_id`, `lesion_id` and configured clinical columns. |
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