Full-scale Representation Guided Network for Retinal Vessel Segmentation

This is official repository of the paper Full-scale Representation Guided Network for Retinal Vessel Segmentation

Environment

• OS: Ubuntu 16.04
• GPU: Tesla V100 32GB
• GPU Driver version: 460.106.00
• CUDA: 11.2
• Pytorch 1.8.1

✅ Experimental Result

Dataset	mIoU	F1 score	Acc	AUC	Sen	MCC
DRIVE	84.068	83.229	97.042	98.235	84.207	81.731
STARE	86.118	85.100	97.746	98.967	86.608	83.958
CHASE_DB1	82.680	81.019	97.515	99.378	85.995	79.889
HRF	83.088	81.567	97.106	98.744	83.616	80.121

✅ Pretrained model for each dataset

Each pre-trained model could be found on release version

🧻 Dataset Preparation

You can edit 'train_x_path...' in "configs/train.yml"
The input and label should be sorted by name, or the dataset is unmatched to learn.

For train/validation set, you can download from public link or release version

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🚄 Train

If you have installed 'WandB', login your ID in command line.
If not, fix 'wandb: false' in "configs/train.yml" You can login through your command line or 'wandb.login()' inside "main.py"

For Train, edit the configs/train.yml and execute below command

bash bash_train.sh

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🛴 Inference

For Inference, edit the configs/inference.yml and execute below command.
Please locate your model path via 'model_path' in "configs/inference.yml"

bash bash_inference.sh

• If you are using pretrained model, the result should be approximate to table's