This project is trained on private MRI data, where scans are marked QC_PASS or QC_FAIL. Several architectures of neural networks are explored to make automatic predictions for new images.
Python
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custom_loss.py
dataviz.py
ibis_reader.py
make_datasets.py
make_ibis.py
ml_experiment.py
ndimage_aug.py
nyul_normalization.py
qc-3d.py
qc-abide-2d.py
qc-ibis-2d.py
qc-ibis-dltk.py
qc-top.py
qc_pytorch_models.py
run_mriqc_docker.py
t1qc.py
temp_scripts.py
temperature_scaling.py
train_deepqc.py
train_ibis_qc.py
train_pytorch_mriqc.py
transformations.py
visualizations.py