For medical image segmentation
- Our model is based on LeViT (https://github.com/facebookresearch/LeViT). You'd better gitclone its codes. Thanks for its great job.
- There are three models which can be build directly. They are LeViT_UNet_128s, LeViT_UNet_192, and LeViT_UNet_384. You can build one of the models as it follows:
model = Build_LeViT_UNet_192(num_classes=9, pretrained=True)
model.eval()
output = model(torch.randn(1, 1, 224, 224))
If you use this code for a paper please cite:
@article{LeViT-UNet,
author = {Guoping Xu and
Xingrong Wu and
Xuan Zhang and
Xinwei He},
title = {LeViT-UNet: Make Faster Encoders with Transformer for Medical Image
Segmentation},
journal = {CoRR},
volume = {abs/2107.08623},
year = {2021},
url = {https://arxiv.org/abs/2107.08623},
eprinttype = {arXiv},
eprint = {2107.08623},
timestamp = {Thu, 22 Jul 2021 11:14:11 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2107-08623.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}