PFENet

PFENet: Progressive Feature Enhancement Network for Automated Colorectal Polyp Segmentation

Authors: Guanghui Yue, Houlu Xiao

1. Preface

This repository provides code for "Progressive Feature Enhancement Network for Automated Colorectal Polyp Segmentation" IEEE TASE-2024.

if you have any questions about our paper, feel free to contact me. And if you are using PraNet or evaluation toolbox for your research, please cite this paper

2. Overview

2.1. Framework Overview

2.2. Qualitative Results

3. Proposed Baseline

3.1. Testing

The testing experiments are conducted using PyTorch with a single GeForce RTX TITAN GPU of 24 GB Memory.

1.Configuring your environment (Prerequisites):

Creating a virtual environment in terminal: conda create -n PraNet python=3.8.

Installing necessary packages: PyTorch 2.0

2.Downloading necessary data:

downloading testing dataset and move it into ./data/TestDataset/, which can be found in this BaiduNetDisk. 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 BaiduNetDisk. It contains two sub-datasets: Kvasir-SEG (900 train samples) and CVC-ClinicDB (550 train samples).

downloading testing weights and move it into ./snapshots/mynetwork.pth, which can be found in this BaiduNetDisk.

3.Testing Configuration:

After you download all the pre-trained model and testing dataset, just run etest.py to generate the final prediction map and calculate metrics including Dice, IoU, Sm, Em, Fm, MAE: replace your trained model directory (--pth_path).

Just enjoy it!

4. Citation

5. License