The code repository for paper "Anatomical Context Protects Against Adversarial Perturbations on Medical Imaging Deep Learning"
To run the experiments, some parameters are needed.
The first parameter is the attack method we want to run. In this repository, four attacks are implemented.
- gsm_exp: the
attack for a single image
- l2_exp: the
attack for a single image
- l0_exp: the
attack for a single image
- multi_gsm: the
The second parameter is whether the model we want to attack used the MAS features. If the value with-mas is True, the model use the image data with the MAS features to predict age. Otherwise no features would be used.
The third parameter is used to choose whether we want to maximize the prediction age or minimize the prediction age by adding the perturbation. The value can be chosen to be max or min.
We can also choose the instances to attack by specifying the range [start, start+instances] and whether a sample perturbation would be saved by specifying the value of save-data.
As an examples, to maximize the prediction age in the model with MAS features using l2 attack, we can do
python main.py --mode=l2_exp --with-mas=true --direction=max --start=0 --instances=1 --save-data=true
The code works on Python 3.5.3.
All models were trained on an aggregate of healthy controls from 7 publicly available T1 MRI datasets:
ABIDE 1: http://fcon_1000.projects.nitrc.org/indi/abide/abide_I.html
ADHD200: http://fcon_1000.projects.nitrc.org/indi/adhd200/
FCON-1000: http://fcon_1000.projects.nitrc.org/fcpClassic/FcpTable.html
IXI: http://brain-development.org/ixi-dataset/
NDAR: https://ndar.nih.gov/study.html?id=318
NKI Rockland: http://fcon_1000.projects.nitrc.org/indi/enhanced/neurodata.html
OASIS1: https://www.oasis-brains.org/