LaTiM: Longitudinal representation learning in continuous-time models to predict disease progression

Zeghlache, R., Conze, P., El Habib Daho, M., Li, Y., Le Boité, H., Tadayoni, R., Massin, P., Cochener, B., Rezaei, A., Brahim, I., & others

International Conference on Medical Image Computing and Computer-Assisted Intervention, 2024 : 404-414

Abstract

Introduction

Methods

Results

Discussion

Conclusions

Acknowledgments

Citation

Zeghlache, R., Conze, P., El Habib Daho, M., Li, Y., Le Boité, H., Tadayoni, R., Massin, P., Cochener, B., Rezaei, A., Brahim, I., and others (2024). LaTiM: Longitudinal representation learning in continuous-time models to predict disease progression. International Conference on Medical Image Computing and Computer-Assisted Intervention.

@inproceedings{zeghlache2024latim,
  title={LaTiM: Longitudinal representation learning in continuous-time models to predict disease progression},
  author={Zeghlache, Rachid and Conze, Pierre-Henri and El Habib Daho, Mostafa and Li, Yihao and Le Boité, Hugo and Tadayoni, Ramin and Massin, Pascale and Cochener, Béatrice and Rezaei, Alireza and Brahim, Ikram and others},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={404--414},
  year={2024},
  organization={Springer Nature Switzerland Cham},
}