Introduction

• Pytorch implementation for paper Treatment Outcome Prediction for Intracerebral Hemorrhage via Generative Prognostic Model with Imaging and Tabular Data

Abstract

Intracerebral hemorrhage (ICH) is the second most common and deadliest form of stroke. Despite medical advances, predicting treatment outcomes for ICH remains a challenge. This paper proposes a novel prognostic model that utilizes both imaging and tabular data to predict treatment outcome for ICH. Our model is trained on observational data collected from non-randomized controlled trials, providing reliable predictions of treatment success. Specifically, we propose to employ a variational autoencoder model to generate a low-dimensional prognostic score, which can effectively address the selection bias resulting from the non-randomized controlled trials. Importantly, we develop a variational distributions combination module that combines the information from imaging data, non-imaging clinical data, and treatment assignment to accurately generate the prognostic score. We conducted extensive experiments on a real-world clinical dataset of intracerebral hemorrhage. Our proposed method demonstrates a substantial improvement in treatment outcome prediction compared to existing state-of-the-art approaches.

Setup

OS Requirements

This model has been tested on the following systems:

• Linux: Ubuntu 18.04

Package                Version
---------------------- -------------------
torch                  1.4.0
torchvision            0.5.0
h5py                   3.1.0
opencv-python          4.5.2.52
SimpleITK              2.0.2
scikit-image.          0.17.2
ml-collections         0.1.1
tensorboardx           2.2.0
medpy                  0.4.0
scikit-learn           0.24.2
pandas                 1.1.5

Training & Testing

• This article uses a private dataset. In order to successfully run the code, you need to prepare your own dataset.
• Specifically, you need to prepare a .xls file, which saves the patients' non-imaging clinical data and the path of imaging data. We have provided an example for you to run the data, which is saved in "./data/IPH/example.xls".
• We run main_VAE.py to train and evaluate the model:

python main_VAE.py

• Our proposed model is saved in models.py, named "VAE_MM".

Citation

If this repository is useful for your research, please cite:

   @inproceedings{ma2023treatment,
     title={Treatment Outcome Prediction for Intracerebral Hemorrhage via Generative Prognostic Model with Imaging and Tabular Data},
     author={Ma, Wenao and Chen, Cheng and Abrigo, Jill and Mak, Calvin Hoi-Kwan and Gong, Yuqi and Chan, Nga Yan and Han, Chu and Liu, Zaiyi and Dou, Qi},
     booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
     pages={715--725},
     year={2023},
     organization={Springer}
   }

Contact

For any questions, please contact 'wama@cse.cuhk.edu.hk'

License

This project is covered under the Apache 2.0 License.