Related paper

CFPNet-M: A Light-Weight Encoder-Decoder Based Network for Multimodal Biomedical Image Real-Time Segmentation

Paper in Arxiv

This repository contains the implementation of a novel light-weight real-time network (CFPNet-Medicine or CFPNet-M) to segment different types of biomedical images. The dataset we used are DRIVE, ISBI-2012, Infrared Breast, CVC-ClinicDB and ISIC 2018.

Architecture of CFPNet-M

CFP module

CFPNet-M

Usage

Prerequisities

The following dependencies are needed:

• Kearas == 2.2.4
• Opencv == 3.3.1
• Tensorflow == 1.10.0
• Matplotlib == 3.1.3
• Numpy == 1.19.1

training

You can download the datasets you want to try, and just run: for UNet, DC-UNet, MultiResUNet, ICNet, CFPNet-M, ESPNet and ENet, the code is in the folder network. For Efficient-b0, MobileNet-v2 and Inception-v3, the code is in the main.py. Choose the segmentation model you want to test and run:

main.py

Segmentation Results of Five datasets

In this project, we test five datasets:

• Infrared Breast Dataset
• Endoscopy (CVC-ClinicDB)
• Electron Microscopy (ISBI-2012)
• Drive (Digital Retinal Image)
• Dermoscopy (ISIC-2018)

Speed and FLOPs

The code of test speed and FLOPs are in main.py, you can run them after training.