Brain MRI tumour segmentation and fMRI autism classification: training images (CPU and CUDA 12.6).
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Label-efficient learning on brain MRI and resting-state fMRI: tumour segmentation with a bilateral 3D U-Net (MSD Task01) and autism classification from connectomes with a noise-aware linear model (ABIDE I).
Code, documentation and the interactive dashboard: https://github.com/mahdisabetkish/brainssl Dashboard: https://mahdisabetkish.github.io/brainssl/
| Tag | Contents | Size |
|---|---|---|
cpu, latest | Python 3.11, PyTorch (CPU), the brainssl package. Enough for the connectome study, the tests and the synthetic pipeline test | about 0.5 GB |
gpu | Same package with PyTorch for CUDA 12.6, for segmentation training | about 3.3 GB |
docker pull mahdisabetkish/brainssl:cpu
docker run --rm mahdisabetkish/brainssl:cpu brainssl --help
# with a GPU and your data folder mounted
docker run --rm --gpus all -v "$PWD:/work" -w /work mahdisabetkish/brainssl:gpu brainssl prepare msd
The images hold code only. Data are downloaded with brainssl download, and the licences of the datasets
(CC BY-SA 4.0 for MSD, CC BY-NC-SA for ABIDE) apply to them. Research use only, not a medical device.
Content type
Image
Digest
sha256:a79ba3603…
Size
447.5 MB
Last updated
9 days ago
docker pull mahdisabetkish/brainssl