GLoG-CSUnet: Enhancing Vision Transformers with Adaptable Radiomic Features for Medical Image Segmentation

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This repository contains the benchmarks, results, and all the code required to reproduce the results, tables, and figures presented in our paper.

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1. Prepare dataset.

create a folder named "data" in the root directory of the project and download the datasets from the following links:

• Synapse dataset can be found at the repo of TransUnet.

• ACDC dataset can be found at the repo of MT-Unet

place the downloaded datasets in the data\ACDC or data\Synapse folder.

2. Clone the code

• First, clone our code with:

git clone [email protected]:HAAIL/GLoG-CSUnet.git
cd GLoG-CSUnet

3. Install requirements

• Install the required packages with:

pip3 install -r requirements.txt

4. Start training

• After that, you can start training with:

python3  train_CSUnet_ACDC.py 

or

python3  train_CSUnet_Synapse.py 

The weights will be saved to "./checkpoint/"

Bibtex

@inproceedings{eghbali2025conformaldqn,
  title={GLoG-CSUnet: Enhancing Vision Transformers with Adaptable Radiomic Features for Medical Image Segmentation},
  author={Eghbali, Niloufar and Bagher-Ebadian, Hassan and  Alhanai, Tuka and Ghassemi, Mohammad M},
  booktitle={IEEE international conference on acoustics, speech and signal processing (ICASSP)},
  year={2025}
}

References

CS-Unet
Swin-Unet
TransUnet