Awesome
InsMix
<!-- [[paper](https://arxiv.org/abs/1905.06696).] -->This is the official code for "InsMix: Towards Realistic Generative Data Augmentation for Nuclei Instance Segmentation (MICCAI 2022, early accepted)"
Pipeline
Method
Requirements
torch>=1.4.0 torchvision>=0.5.0 dominate>=2.4.0 visdom>=0.1.8.8 wandb
Usage
InsMix w/o Smooth-GAN
The fuctions 'insmix' and 'background shuffle' can be found in 'data_aug.py'. The example code for dataloader is in 'dataset.py'. Note that it can be used to BRPNet and NB-Net, which utilize two types of label, i.e., the inner area and the boundary.
InsMix w/ Smooth-GAN
You may simply run the scripts as:
bash Smooth-GAN/scripts/train_nuclei.sh
bash Smooth-GAN/scripts/test_nuclei.sh
Citation
Pleae cite the paper if you use the code.
@inproceedings{lin2022insmix,
title={{InsMix}: Towards Realistic Generative Data Augmentation for Nuclei Instance Segmentation},
author={Lin, Yi and Wang, Zeyu and Cheng, Kwang-Ting and Chen, Hao},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
year={2022},
organization={Springer}
}
TODO
- Training and testing on Kumar dataset.
- Refactor the code to make it more readable.
Acknowledgment
The code of Smooth-GAN is heavily build on pix2pix, thanks for their amazing work!