EVT-MIL: Deep Multi-Instance Volumetric Image Classification with Extreme Value Distributions

Running the code

The code was tested in Keras with Tensorflow backend. The packages needed are listed in the requirements.txt (not all packages in file are used. Just did a pip freeze for my virtual environment.)

Installing python virtual environment and requirements

pip install virtualenv
virtualenv --no-site-packages vkeras
source vkeras/bin/activate
pip install -r path/to/requirements.txt

Prepare data

For each data-set:

• Convert each instance into a numpy array with dimensions [height, width, channels] and save in a pre specified folder.
• Create a csv file containing the following columns: ['instance_file_name', 'bag_name', 'bag_label', <additional information,>, 'Cross-validation_split'] for each instance. instance_file_name can be bag_name + a unique number, Cross-validation_split should be a number starting from zero.

Training the network and obtaining validation results

• Adjust hyper parameters and paths defined in the file hyperparameters.py
• Run train_3d_evt_mil_cv.py

The AUC values for validation data, at each epoch, will be writen to a .csv file in ./outputs folder

Publication

If you find this work useful in your research, please consider citing:

@ARTICLE{8805413,
author={R. {Tennakoon} and G. {Bortsova} and S. {Ørting} and A. K. {Gostar} and M. M. W. {Wille} and Z. {Saghir} and R. {Hoseinnezhad} and M. {de Bruijne} and A. {Bab-Hadiashar}},
journal={IEEE Transactions on Medical Imaging},
title={Classification of Volumetric Images Using Multi-Instance Learning and Extreme Value Theorem},
year={2019},
volume={},
number={},
pages={1-1},
doi={10.1109/TMI.2019.2936244},}