README.md

Duplex Contextual Relations for Polyp Segmentation

Introduction

The repository contains the PyTorch implementation of "Duplex Contextual Relations for PolypSegmentation"

Overview

1. Framework

Figure 1: Overview of our proposed DCRNet.

2. Quantitative Results

Figure 2: Qualitative results.

3. Qualitative Results

Figure 3: Quantitative results on EndoScene dataset.

Figure 4: Quantitative results on Kvasir-SEG dataset.

Figure 5: Quantitative results on PICCOLO dataset.

Usage

1. Prerequisite environment

1. torch>=1.5.0

2. torchvision>=0.6.0

3. tqdm

4. scipy

5. scikit-image

6. PIL

7. numpy

8. CUDA

2. Dataset downloading

• Downloading the CVC-EndoSceneStill dataset, which can be found in this Google Drive link
• Downloading the Kvasir-SEG dataset, which can be found in this Google Drive link
• To access the PICCOLO dataset, please visit here

3. Train

• Assign your customized path of --train_path ,--save_root and --gpu in Train.py.
• Run python Train.py

4. Test

• Assign the --pth_path , --data_root , --save_root and --gpu in Test.py.
• Run python Test.py
• The quantitative results will be displayed in your screen, and the qualitative results will be saved in your customized path.

5. Evaluate

• The evaluation code is stored in ./utils/eval.py
• You can replace it with your customized evaluation metrics.