Brain Tumor AI Challenge (2021)
Kaggle: RSNA-MICCAI Brain Tumor Radiogenomic Classification
Author: Haoyi Zhu, Junjie Huang, Haoxuan Sun
# 1. Create a conda virtual enviornment.
conda create -n brain_tumor python=3.9 -y
conda activate brain_tumor
# 2. Install PyTorch >= 1.10 according to your CUDA version. For example:
pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113
# 3. Get the repo
git clone https://github.com/HaoyiZhu/BrainTumor.git
cd BrainTumor
# 4. Install
pip install -e .
First get kaggle API by running pip install kaggle
, then you may need to register your kaggle account following here. Next download brain tumor segmentation and classification datasets:
kaggle competitions download -c rsna-miccai-brain-tumor-radiogenomic-classification
kaggle datasets download -d dschettler8845/brats-2021-task1
Finally, extract the datasets to ./data
and make them look like this:
|-- brain_tumor
|-- configs
|-- scripts
|-- train_val_splits
|-- data
`-- |-- classification
| |-- sample_submission.csv
| |-- train_labels.csv
| |-- test
| | |-- 00001
| | |-- 00013
| | |-- ...
| `-- train
| |-- 00000
| |-- 00002
| |-- ...
`-- segmentation
|-- BraTS2021_00495
| |-- BraTS2021_00495_flair.nii.gz
| |-- ...
|-- BraTS2021_00621
| |-- BraTS2021_00621_flair.nii.gz
| |-- ...
`-- BraTS2021_Training_Data
|-- BraTS2021_00000
| |-- BraTS2021_00000_flair.nii.gz
| |-- ...
|-- BraTS2021_00002
| |-- BraTS2021_00002_flair.nii.gz
| |-- ...
|-- ...
# Choose one or multiple gpus
# Number of gpus should match with ${train.devices} in config
# You can simply overwrite the parameters in config by xxx=xxx in command
CUDA_VISIBLE_DEVICES=0,1 python scripts/train.py train.devices=2
# -e means the experiment specification file you choose
# -g means the gpu id you want to use
python scripts/snn_train.py -e configs/snn.yaml -g 0
python scripts/snn_train.py -e configs/snn_new.yaml -g 1