This project is a work in progress.
Install the following:
- NumPy
- pandas
- Scikit-learn
- TensorBoard
- TensorFlow
The input data could be CSV with the following fields:
- features:
- PTV vol (cc)
- Lungs-GTV vol (cc)
- Lungs-GTV-PTV vol (cc)
- Lungs-GTV in PTV vol (cc)
- KBP Lungs (cc)
- Lungs-GTV - KBP Lungs (cc)
- Heart vol (cc)
- Heart in PTV vol (cc)
- targets:
- V5 (%)
- V20 (%)
- Mean Lungs-GTV (Gy)
- V30 (%)
- Mean (Gy)
It could be CSV with the following fields:
- features:
- Dose/#
- Prescription
- PTV vol (cc)
- Lungs-GTV vol (cc)
- Lungs-GTV-PTV vol (cc)
- Lungs-GTV in PTV vol (cc)
- KBP Lungs (cc)
- Lungs-GTV - KBP Lungs (cc)
- targets:
- V5 (%)
- V20 (%)
- Mean Lungs-GTV (Gy)
The rightmost columns should be the targets. The number of targets can be specified as an argument for a neural network script.
Manually remove missing values from data.
Preprocess the CSV data such that all features are scaled to (-1, 1):
./preprocess_CSV_file.py --infile=data.csv --outfile=preprocessed_data.csv
Train and evaluate on preprocessed CSV data with TensorBoard:
./cures_cancer.py --help
./cures_cancer.py --infile=preprocessed_data.csv --TensorBoard