aeroelasticitylu / LRsurvival_CRNN

Code repo for: A hybrid CNN-RNN approach for survival analysis in a Lung Cancer Screening study

A hybrid CNN-RNN approach for survival analysis in a Lung Cancer Screening study

Note Rearranging for public release

A hybrid CNN-RNN approach for survival analysis in a Lung Cancer Screening study [Link to Journal]

DOI: 10.1016/j.heliyon.2023.e18695

Code structure

• util

  • resnet_3d.py: 3D implementation of the ResNet by Chen et al. 2019
  • crnn_model.py: hybrid CNN-RNN models used in the study.

To cite

@article{LU2023e18695,
title = {A hybrid CNN-RNN approach for survival analysis in a Lung Cancer Screening study},
journal = {Heliyon},
volume = {9},
number = {8},
pages = {e18695},
year = {2023},
issn = {2405-8440},
doi = {https://doi.org/10.1016/j.heliyon.2023.e18695},
url = {https://www.sciencedirect.com/science/article/pii/S2405844023059030},
author = {Yaozhi Lu and Shahab Aslani and An Zhao and Ahmed Shahin and David Barber and Mark Emberton and Daniel C. Alexander and Joseph Jacob},
keywords = {Computed tomography, Lung, Deep learning, Computer vision, Saliency map, Longitudinal data, Cox regression}
}