Hyper-Convolution Networks for Biomedical Image Segmentation

Code for our WACV 2022 paper:

Hyper-Convolution Networks for Biomedical Image Segmentation (https://arxiv.org/abs/2105.10559)

and our journal extension published at Medical Image Analysis

Hyper-convolutions via implicit kernels for medical image analysis (https://www.sciencedirect.com/science/article/pii/S1361841523000579)

Convolutional Kernels are generated by a hyper-network instead of independtly learned

The input to the hyper-network are the spatial coordinates of the kernels

requirements:

tensorflow-gpu 1.15.0

python 3.6.13

Code:

To initiate training or testing, run: python main.py --mode train --config_path config.json

--mode train for training, --mode test for testing

--config_path is the path to config json file that contains all model related config

kernal.py contains the input to the hyper-network, which is a two-channels coordinates grid (x and y)

unet_vanilla.py contains all the networks including the baseline UNet, non-local UNet and our method

Citation:

If you find our code useful, please cite our work, thank you!

@inproceedings{ma2022hyper,
  title={Hyper-convolution networks for biomedical image segmentation},
  author={Ma, Tianyu and Dalca, Adrian V and Sabuncu, Mert R},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  pages={1933--1942},
  year={2022}
}

@article{ma2023hyper,
  title={Hyper-convolutions via implicit kernels for medical image analysis},
  author={Ma, Tianyu and Wang, Alan Q and Dalca, Adrian V and Sabuncu, Mert R},
  journal={Medical Image Analysis},
  pages={102796},
  year={2023},
  publisher={Elsevier}
}