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Distribution Matching Losses Can Hallucinate Features in Medical Image Translation

aka How to Cure Cancer (in images) with Unpaired Image Translation

Joseph Paul Cohen, Margaux Luck, Sina Honari

https://arxiv.org/abs/1805.08841

Published at Medical Image Computing & Computer Assisted Intervention (MICCAI 2018). An abstract is published at the Medical Imaging with Deep Learning Conference (MIDL 2018)

How to run

Prepare the data

prepare_data.ipynb

Run the cyclegan for each split

$cd cyclegan
$sh run.sh

Requirements

If you want to use Conda:

conda create -n pytorch python=3 numpy scipy pandas scikit-learn
source activate pytorch
conda install pytorch torchvision cuda80 -c soumith

T-NT Dataset

If you are looking for the dataset used in this paper we have created a dataset called T-NT which contains MRI slides with and without tumors.

Download it here: https://academictorrents.com/details/d52ccc21455c7a82fd6e58964c89b7da99e0edf7

It includes segmentations:

Sample Flair Images

Tumor NoTumor

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