GLoG-CSUnet: Enhancing Vision Transformers with Adaptable Radiomic Features for Medical Image Segmentation
This repository contains the benchmarks, results, and all the code required to reproduce the results, tables, and figures presented in our paper.
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/"
@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}
}