LaTiM: Longitudinal representation learning in continuous-time models to predict disease progression
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arXiv
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| Format: | Preprint |
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2024
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| author | Zeghlache, Rachid Conze, Pierre-Henri Daho, Mostafa El Habib Li, Yihao Boité, Hugo Le Tadayoni, Ramin Massin, Pascal Cochener, Béatrice Rezaei, Alireza Brahim, Ikram Quellec, Gwenolé Lamard, Mathieu |
| author_facet | Zeghlache, Rachid Conze, Pierre-Henri Daho, Mostafa El Habib Li, Yihao Boité, Hugo Le Tadayoni, Ramin Massin, Pascal Cochener, Béatrice Rezaei, Alireza Brahim, Ikram Quellec, Gwenolé Lamard, Mathieu |
| contents | This work proposes a novel framework for analyzing disease progression using time-aware neural ordinary differential equations (NODE). We introduce a "time-aware head" in a framework trained through self-supervised learning (SSL) to leverage temporal information in latent space for data augmentation. This approach effectively integrates NODEs with SSL, offering significant performance improvements compared to traditional methods that lack explicit temporal integration. We demonstrate the effectiveness of our strategy for diabetic retinopathy progression prediction using the OPHDIAT database. Compared to the baseline, all NODE architectures achieve statistically significant improvements in area under the ROC curve (AUC) and Kappa metrics, highlighting the efficacy of pre-training with SSL-inspired approaches. Additionally, our framework promotes stable training for NODEs, a commonly encountered challenge in time-aware modeling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_07091 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LaTiM: Longitudinal representation learning in continuous-time models to predict disease progression Zeghlache, Rachid Conze, Pierre-Henri Daho, Mostafa El Habib Li, Yihao Boité, Hugo Le Tadayoni, Ramin Massin, Pascal Cochener, Béatrice Rezaei, Alireza Brahim, Ikram Quellec, Gwenolé Lamard, Mathieu Machine Learning Artificial Intelligence This work proposes a novel framework for analyzing disease progression using time-aware neural ordinary differential equations (NODE). We introduce a "time-aware head" in a framework trained through self-supervised learning (SSL) to leverage temporal information in latent space for data augmentation. This approach effectively integrates NODEs with SSL, offering significant performance improvements compared to traditional methods that lack explicit temporal integration. We demonstrate the effectiveness of our strategy for diabetic retinopathy progression prediction using the OPHDIAT database. Compared to the baseline, all NODE architectures achieve statistically significant improvements in area under the ROC curve (AUC) and Kappa metrics, highlighting the efficacy of pre-training with SSL-inspired approaches. Additionally, our framework promotes stable training for NODEs, a commonly encountered challenge in time-aware modeling. |
| title | LaTiM: Longitudinal representation learning in continuous-time models to predict disease progression |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2404.07091 |