Evaluating-the-Robustness-of-Self-Supervised-Learning-in-Medical-Imaging

Code will be available soon!!

Source code for our paper "Evaluating-the-Robustness-of-Self-Supervised-Learning-in-Medical-Imaging"

Authors: Fernando Navarro, Christopher Watanabe, Suprosanna Shit, Anjany Sekuboyina, Jan Peeken , Stephanie E. Combs, and Bjoern H. Menze.

Getting Started

Pre-requisites

You need to have following in order for this library to work as expected

1. python >= 3.6.5
2. pip >= 18.1
3. tensorflow-gpu = 1.9.0
4. tensofboard = 1.9.0
5. numpy >= 1.15.0
6. dipy >= 0.14.0
7. matplotlib>= 2.2.2
8. nibabel >= 1.15.0
9. pandas >= 0.23.4
10. scikit-image >= 0.14.0
11. scikit-learn >= 0.20.0
12. scipy >= 1.1.0
13. seaborn >= 0.9.0
14. SimpleITK >= 1.1.0
15. tabulate >= 0.8.2
16. xlrd >= 1.1.0

Install requirements

Run pip install -r requirements.txt

How to use the code for training

Convert your data-set to npz

Start the training

Run the python script train_val.py. Make sure to change file paths for tfrecord files according to your configuration.

How to use the code for inference

Run the python script inference.py. Follow the commends in the script to change variables according to your training model and file paths.

License and Citation

Please cite our paper if it is useful for your research:

Code Authors

• Fernando Navarro - ferchonavarro
• Christopher Watanabe - []

Help us improve

Let us know if you face any issues. You are always welcome to report new issues and bugs and also suggest further improvements. And if you like our work hit that start button on top. Enjoy :)