This repository contains the code of GLIMS.
GLIMS ranked in the top 5 among 65 unique submissions during the validation phase of the Adult Glioblastoma Segmentation challenge of BraTS 2023.
git clone https://github.com/yaziciz/GLIMS.git
cd GLIMS
With your virtual environment activated, install the project's dependencies:
pip install -r requirements.txt
The GLIMS model can be trained by the given script on the BraTS 2023 dataset:
python main.py --output_dir <output_directory> --data_dir <data_directory> --json_list <json_list_file> --fold <fold_id>
By using the pre-trained model, the validation phase can be performed as follows:
python post_validation.py --output_dir <output_directory> --data_dir <data_directory> --json_list <json_list_file> --fold <fold_number> --pretrained_dir <pretrained_model_directory>
To test GLIMS by using the ensemble method on the unannotated BraTS 2023 dataset, the following script can be used:
python test_BraTS.py --data_dir <validation_data_directory> --model_ensemble_1 <model_1_path> --model_ensemble_2 <model_2_path> --output_dir <output_directory>
The model_ensemble_1
and model_ensemble_2
variables represent the fold 2
and fold 4
models, as indicated in our challenge submission paper on arXiv.
GLIMS: Attention-guided lightweight multi-scale hybrid network for volumetric semantic segmentation
Image and Vision Computing, May 2024
Journal Paper, arXiv
@article{yazici2024glims,
title={GLIMS: Attention-guided lightweight multi-scale hybrid network for volumetric semantic segmentation},
author={Yazici, Ziya Ata and Oksuz, Ilkay and Ekenel, Hazim Kemal},
journal={Image and Vision Computing},
pages={105055},
year={2024},
publisher={Elsevier},
doi={https://doi.org/10.1016/j.imavis.2024.105055}
}
Attention-Enhanced Hybrid Feature Aggregation Network for 3D Brain Tumor Segmentation
Accepted to the 9th Brain Lesion (BrainLes) Workshop @ MICCAI 2023
arXiv
@article{yazici2024attention,
title={Attention-Enhanced Hybrid Feature Aggregation Network for 3D Brain Tumor Segmentation},
author={Yazici, Ziya Ata and Oksuz, Ilkay and Ekenel, Hazim Kemal},
journal={arXiv preprint arXiv:2403.09942},
year={2024}
}
Thank you for your interest in our work!
We are also deeply grateful to the MONAI Consortium for their MONAI framework, which was instrumental in the development of GLIMS.