README

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

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?

• raghav@di.ku.dk

Thanks

Some parts of our implementation are based on:

• Prob.U-Net and LIDC data
• Planar flows
• Glow model