/cFlow

This is official Pytorch implementation of "Uncertainty quantification in medical image segmentation with Normalizing Flows", Raghavendra Selvan et al. 2020

Primary LanguagePythonMIT LicenseMIT

README

This is official Pytorch implementation of "Uncertainty quantification in medical image segmentation with Normalizing Flows", Raghavendra Selvan et al. 2020

lotenet

What is this repository for?

  • Train the proposed model on LIDC and Retina datasets
  • Reproduce the reported numbers in the paper
  • v1.0

How do I get set up?

  • Basic Pytorch dependency
  • Tested on Pytorch 1.3, Python 3.6
  • Download preprocessed LIDC dataset from here. ** Change the file name with .zip after downloading. **

Usage guidelines

  • Kindly cite our publication if you use any part of the code
@inproceedings{raghav2020cFlowNet,
 	title={Uncertainty quantification in medical image segmentation with Normalizing Flows},
	author={Raghavendra Selvan, Frederik Faye, Jon Middleton, Akshay Pai},
 	booktitle={11th International Workshop on Machine Learning in Medical Imaging},
	month={October},
	year={2020}
	url={https://arxiv.org/abs/2006.02683}}

Who do I talk to?

Thanks

Some parts of our implementation are based on: