This is the code repository for the paper "Pediatric Age Estimation from Thoracic and Abdominal CT Scout Views using Deep Learning".
All coding was done using Python 3.8.10. Needed modules can be found in requirements.txt.
Because of privacy, no images and no code for preparing these have been uploaded. Images should be put into ./images; corresponding data frames containing the image path and the ages should be put into ./data. The dataset.py file must be adapted correspondingly.
Five different methods were tested: Simple regression using L1 loss, regression using L1 loss but also DICOM tags, and three more sophisticated methods, AMR, CORAL and CORN. For each of these methods, a hyperparameter tuning was performed using Optuna 3.0. To start tuning, execute ./train_optuna.py in each of the subdirectories. This will create a CSV with the performance of each tested network.
Afterwards the best model is retrained on all data by using ./retrain.py Actually this method first reproduced the best model (to check whether everything works and is reproducable) and then retrains and evaluates on all data. Only the evaluation of the best-performing model is considered-- I did not care about the MAE of the other models, but out of comfort, still train and evaluate them.
For evaluation, call the ./evaluate.py script. For the visualization, execute ./visualize.py. For the equivalence test, you will need R and the TOSTER library. The script is eqTest.R.
Results of the optuna run and the final predictions of all models on the validation and test set can be found in ./results
In addition, the pngs of all generated figures can be found in ./paper.
Copyright 2022, Aydin Demircioglu
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