Dense Steerable Filter CNNs for Expoiting Rotational Symmetry in Histology Images

A densely connected rotation-equivariant CNN for histology image analysis.

Link to the pre-print.

NEWS: Our paper has now been published in IEEE Transactions on Medical Imaging. Find the published article here.

Getting Started

Environment instructions:

conda create --name dsf-cnn python=3.6
conda activate dsf-cnn
pip install -r requirements.txt

Repository Structure

• src/ contains executable files used to run the model. Further information on running the code can be found in the corresponding directory.
• loader/contains scripts for data loading and self implemented augmentation functions.
• misc/contains util scripts.
• model/class_pcam/ model architecture for dsf-cnn on PCam dataset
• model/seg_nuc/ model architecture for dsf-cnn on Kumar dataset
• model/seg_gland/ model architecture for dsf-cnn on CRAG dataset
• model/utils/ contains util scripts for the models.
• opt/ contains scripts that define the model hyperparameters and augmentation pipeline.
• config.py is the configuration file. Paths need to be changed accordingly.
• train.py and infer.py are the training and inference scripts respectively.
• process.py is the post processing script for obtaining the final instances for segmentation.

Citation

If any part of this code is used, please give appropriate citation to our paper.

@article{graham2020dense,
  title={Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images},
  author={Graham, Simon and Epstein, David and Rajpoot, Nasir},
  journal={arXiv preprint arXiv:2004.03037},
  year={2020}
}

Authors

See the list of contributors who participated in this project.

License

This project is licensed under the MIT License - see the LICENSE file for details