MJAHMADEE/BG-Net

this is a demo

BG-Net

this is a demo

Boundary-aware Gradient Operator Network for Medical Image Segmentation

News

2023.5.28: The BG-Net model has been optimised. The paper will be updated later.

Requirements

• PyTorch 1.x or 0.41

Installation

1. Create an anaconda environment.

conda create -n=<env_name> python=3.6 anaconda
conda activate <env_name>

2. Install PyTorch.

conda install pytorch torchvision cudatoolkit=10.1 -c pytorch

3. Install pip packages.

pip install -r requirements.txt

Training on 2018 Data Science Bowl dataset

1. Download dataset from here to inputs/ and unzip. The file structure is the following:

inputs
└── ISIC2018
    ├── images
                ├──ISIC_00000001.png
                ├──ISIC_00000002.png
                ├──ISIC_0000000n.png
          ├── masks
                ├──ISIC_00000001.png
                ├──ISIC_00000002.png
                ├──ISIC_0000000n.png
    ...

2. Train the model.
```sh
python train.py --dataset ISIC2018 --arch BG-Net

3. Evaluate.

python text.py --name ISIC2018-model