H2Former

This repository contains the implementation of our paper "H2Former: An Efficient Hierarchical Hybrid Transformer for Medical Image Segmentation"

Requirements

python 3.6

numpy 1.16.4

Pytorch 1.8.1

pillow 7.0.0

opencv-python 4.1.0

Usage

1. Clone the repository, and download the pre-trained ImaenNet model, put them into ./ folder. The details of the training are in train.py file.

2. And then run the code：python train.py Note that the parameters and paths should be set beforehand

3. Once the training is complete, you can run the test.py to test your model. Run the code : python test.py.

LICENSE

Code can only be used for ACADEMIC PURPOSES. NO COMERCIAL USE is allowed. Copyright © College of Computer Science, Nankai University. All rights reserved.

Citation