Lmt: Longitudinal mixing training, a framework to predict disease progression from a single image

Zeghlache, R., Conze, P., Daho, M., Li, Y., Le Boité, H., Tadayoni, R., Massin, P., Cochener, B., Brahim, I., Quellec, G., & others

International Workshop on Machine Learning in Medical Imaging, 2023 : 22-32

Abstract

Introduction

Methods

Results

Discussion

Conclusions

Acknowledgments

Citation

Zeghlache, R., Conze, P., Daho, M., Li, Y., Le Boité, H., Tadayoni, R., Massin, P., Cochener, B., Brahim, I., Quellec, G., and others (2023). Lmt: Longitudinal mixing training, a framework to predict disease progression from a single image. International Workshop on Machine Learning in Medical Imaging.

@inproceedings{zeghlache2023lmt,
  title={Lmt: Longitudinal mixing training, a framework to predict disease progression from a single image},
  author={Zeghlache, Rachid and Conze, Pierre-Henri and Daho, Mostafa El Habib and Li, Yihao and Le Boité, Hugo and Tadayoni, Ramin and Massin, Pascal and Cochener, Béatrice and Brahim, Ikram and Quellec, Gwenolé and others},
  booktitle={International Workshop on Machine Learning in Medical Imaging},
  pages={22--32},
  year={2023},
  organization={Springer Nature Switzerland Cham},
}