Skip to content

Latest commit

 

History

History
62 lines (49 loc) · 3.13 KB

README.md

File metadata and controls

62 lines (49 loc) · 3.13 KB
Raw

Introduction

intro

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 '[email protected]'

License

This project is covered under the Apache 2.0 License.