Hybrid Graph Mamba: Unlocking Non-Euclidean Potential for Accurate Polyp Segmentation

Early Accepted By MICCAI 2025

• HGM <p align="center"> <img src="imgs/model.png"/> <br /> </p>

Training/Testing

The training and testing experiments are conducted using PyTorch with two GeForce RTX 3090 GPUs of 24 GB Memory.

Note that our model also supports low memory GPU, which means you can lower the batch size

1. Configuring your environment (Prerequisites):

   Note that HGM is only tested on Ubuntu OS with the following environments. It may work on other operating systems as well but we do not guarantee that it will.

   • Creating a virtual environment in terminal: conda create -n HGM python=3.10.

   • Installing necessary packages: pip install -r requirements.txt .

2. Downloading necessary data:

   • downloading testing dataset and move it into ./data/TestDataset/, which can be found in this Google Drive Link (327.2MB). It contains five sub-datsets: CVC-300 (60 test samples), CVC-ClinicDB (62 test samples), CVC-ColonDB (380 test samples), ETIS-LaribPolypDB (196 test samples), Kvasir (100 test samples).

   • downloading training dataset and move it into ./data/TrainDataset/, which can be found in this Google Drive Link (399.5MB). It contains two sub-datasets: Kvasir-SEG (900 train samples) and CVC-ClinicDB (550 train samples).

   • downloading pretrained weights and move it into ./best_parameter_HGM.pth, which can be found in this Google Drive Link (101.9MB).

3. Training Configuration:

   • Assigning your costumed path, like --train_save, --train_path, --train_save, and --results_save_place in MyTrain.py.

   • Just enjoy it!

4. Testing Configuration:

   • After you download all the pre-trained model and testing dataset, just run MyTest.py to generate the final prediction map: replace your trained model directory (--pth_path).

   • Just enjoy it!