This repository contains the code of our research on prognostic AI-monitoring: a prototype for automatic response evaluation to treatment of cancer patients with advanced disease based on deep learning image-to-image registration.
🚧 This research is still in its preliminary phase, further development and validation is warrant before clinical use.
- Python 3.6
- Tensorflow 1.15.0
- Keras
- Scikit-Learn
- Pandas
- SimpleITK
VoxelMorph, Neuron and Frida are already included in the libs folder.
Parts of Keras-Group-Normalization and Recursive-Cascaded-Networks are reused in the main code.
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Virtual environment
$ conda create --name tf-1.15 $ conda activate tf-1.15 -
Installing packages inside the virtual environment
$ conda install -c anaconda tensorflow-gpu==1.15 $ conda install -c anaconda scikit-learn $ conda install -c anaconda pandas $ conda install -c simpleitk simpleitk $ conda install -c conda-forge keras $ conda install -c conda-forge nibabel $ conda install -c conda-forge tqdm $ conda install -c anaconda pillow $ conda install -c conda-forge matplotlib
Stefano Trebeschi, Zuhir Bodalal, Thierry N. Boellaard, Teresa M. Tareco Bucho, Silvia G. Drago, Ieva Kurilova, Adriana M. Calin-Vainak, Andrea Delli Pizzi, Mirte Muller, Karlijn Hummelink, Koen J. Hartemink, Thi Dan Linh Nguyen-Kim, Egbert F. Smit, Hugo J. Aerts and Regina G. Beets-Tan; Prognostic value of deep learning mediated treatment monitoring in lung cancer patients receiving immunotherapy, Frontiers in Oncology, Cancer Imaging and Imaging directed Interventions, 2021 doi: 10.3389/fonc.2021.609054 (it's open access!)
Stefano Trebeschi, Zuhir Bodalal, Nick van Dijk, Thierry N. Boellaard, Paul Apfaltrer, Teresa M. Tareco Bucho, Thi Dan Linh Nguyen-Kim, Michiel S. van der Heijden, Hugo J. W. L. Aerts and Regina G. H. Beets-Tan; Development of a Prognostic AI-Monitor for Metastatic Urothelial Cancer Patients Receiving Immunotherapy, Frontiers in Oncology, Genitourinary Oncology, 2021 doi: 10.3389/fonc.2021.637804 (it's also open access!)