DualNet-Lesion-Segmentation: Dual-Branch Network with Dual-Sampling Modulated Dice Loss for Hard Exudate Segmentation in Colour Fundus Images

This repository is the official PyTorch implementation of paper: Dual-Branch Network with Dual-Sampling Modulated Dice Loss for Hard Exudate Segmentation in Colour Fundus Images .

Code Structure

• The dual-HED folder contains vanilla and DualNet versions of HED.
• The dual-segmentation-toolbox folder contains vanilla and DualNet versions of PSPNet and Deeplabv3.
• The preprocessing and evaluation scripts can be found in scripts folder.

Environment

• Python: 3.7

• PyTorch: 1.1.0

• The dual-segmentation-toolbox code also needs apex and inplace-abn.

```sh conda create -n dualnet python=3.7 -y conda activate dualnet

pip install torch==1.1.0 torchvision==0.3.0

cd apex python setup.py install --cppext pip install inplaceabn==1.0.12

pip install opencv-python pip install tqdm scipy scikit-image

for evaluation scripts

pip install pandas sklearn pip install cupy-cuda100 ```

The ImageNet pretrained weights of backbones are available at this link.

Training and Evaluation

Please refer to train.sh and eval.sh.

Results and models

We evaluate our DualNet on DDR and IDRiD datasets.

DDR

| Method | IoU | F<sub>pixel</sub> | AUPR | |:-------------------|:-----:|:-----------------:|:-----:| | Dual PSPNet+DSM | 40.40 | 57.55 | 54.86 | | Dual DeepLabV3+DSM | 38.62 | 55.72 | 52.29 | | Dual HED+DSM | 41.39 | 58.55 | 49.90 |

IDRiD

| Method | IoU | F<sub>pixel</sub> | AUPR | |:-------------------|:-----:|:-----------------:|:-----:| | Dual PSPNet+DSM | 61.03 | 75.80 | 77.82 | | Dual DeepLabV3+DSM | 61.53 | 76.19 | 76.69 | | Dual HED+DSM | 61.16 | 75.90 | 79.05 |

The corresponding trained weights of our DualNet models are available at this link.

In the paper, we reported average performance over three repetitions, while our code only reported the best one among them.

Acknowledgements

This code is heavily borrowed from HED , pytorch-segmentation-toolbox, and BBN. Thanks for their contributions.

Citation

If you find this code useful in your research, please consider citing:

@article{liu2022dual, title={Dual-Branch Network With Dual-Sampling Modulated Dice Loss for Hard Exudate Segmentation in Color Fundus Images}, author={Liu, Qing and Liu, Haotian and Zhao, Yang and Liang, Yixiong}, journal={IEEE Journal of Biomedical and Health Informatics}, volume={26}, number={3}, pages={1091--1102}, year={2022}, publisher={IEEE}， doi={10.1109/JBHI.2021.3108169} }