F-UNet: Deep convolutional neural network based on feature engineering for medical image segmentation

Referring to the idea of Feature Engineering in traditional machine learning, we propose a deep convolution neural network based on Feature Engineering(F-UNet). The key of F-UNet is to alleviate the scarcity of annotated data by constructing the diversity of feature engineering. F-UNet is divided into two stages. In the first stage, a basic segmentation network is trained to build more diverse feature engineering. In the second stage, the improved network is trained by the diversity feature engineering constructed in the first stage.

Paper

This repository provides the official Pytorch implementation of F-UNet in the following papers:

F-UNet: Deep convolutional neural network based on feature engineering for medical image segmentation
Xutao Guo, Ting Ma
Harbin Institute of Technology at Shenzhen

Folder

• data:the folder where dataset is placed.
• models:model files.
• check:utils files(include many utils)
  • main:training, validation and test function.
  • sets:some configuration about project parameters.

Prerequisites

• PyTorch 1.0
  conda install torch torchvision
• tqdm
  conda install tqdm
• imgaug
  conda install six numpy scipy Pillow matplotlib scikit-image opencv-python imageio Shapely
conda install imgaug