We analyze data provided by The Human Connectome Project (HCP). Using T1-weighted MRI data we solve the problem of binary classification of task of gender patterns recognition between men and women with 3D - CNN. Further we intepret obtained model to undestand of gender-related brain differencies.
Please refer to the sourse paper.
Install all dependencies with
pip install -r ./requirements.txt
The data we use is an open-access database taken from Human Connection Project (HCP). We worked with morphometry description of T1 MPI images as wel as the full-sized images preprocessed in Freesurfer
according to the HCP pipeline.
Data contain 1113 subjects, including 507 men and 606 women. Each object is represented by a 1 GB ZIP archive with a name corresponding to a unique object ID. Each archive contains a lot of information. For automatic access to the target MRI file, the power shell script was written that can be found in
./data/DATE_ACCESS.md
The script allows you to extract the necessary file from the internal ZIP archive(inside the main archive), without unzipping the main one. Also, a unique ID corresponding to each object is assigned as a name for each file.
To obtain all masks use
./masks/obtain_masks.ipynb
To train the 3D CNN models use
./model3d/training_model.ipynb
Architecture reproduced from the paper Brain Differences Between Men and Women: Evidence From Deep Learning
Interpretation with meaningful perturbation:
Interpretation with Grad CAM:
3D CNN Interpretation with Guided backpropogation: