Awesome
MISSFormer
Code for paper "MISSFormer: An Effective Medical Image Segmentation Transformer". Please read our preprint at the following link: paper_address. Our paper has been accepted by TMI. More detailed comparative experiments and analysis can be found in the early access journal paper:tmi_paper_address.
1. Environment
- Please prepare an environment with Ubuntu 20.04, with Python 3.6.13, PyTorch 1.8.0, and CUDA 11.1.1.
2. Train/Test
- Train
python train.py --dataset Synapse --root_path your DATA_DIR --max_epochs 400 --output_dir your OUT_DIR --img_size 224 --base_lr 0.05 --batch_size 24
- Test
python test.py --dataset Synapse --is_savenii --volume_path your DATA_DIR --output_dir your OUT_DIR --max_epoch 400 --base_lr 0.05 --img_size 224 --batch_size 24
References
@article{huang2021missformer,
title={MISSFormer: An Effective Medical Image Segmentation Transformer},
author={Huang, Xiaohong and Deng, Zhifang and Li, Dandan and Yuan, Xueguang},
journal={arXiv preprint arXiv:2109.07162},
year={2021}
}