Summary of Latim: Longitudinal Representation Learning in Continuous-time Models to Predict Disease Progression, by Rachid Zeghlache et al.
LaTiM: Longitudinal representation learning in continuous-time models to predict disease progression
by Rachid Zeghlache, Pierre-Henri Conze, Mostafa El Habib Daho, Yihao Li, Hugo Le Boité, Ramin Tadayoni, Pascal Massin, Béatrice Cochener, Alireza Rezaei, Ikram Brahim, Gwenolé Quellec, Mathieu Lamard
First submitted to arxiv on: 10 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed novel framework utilizes time-aware neural ordinary differential equations (NODE) to analyze disease progression. The “time-aware head” is introduced in the framework trained through self-supervised learning (SSL), leveraging temporal information in latent space for data augmentation. This approach effectively integrates NODEs with SSL, offering significant performance improvements compared to traditional methods lacking explicit temporal integration. The strategy demonstrates its effectiveness for diabetic retinopathy progression prediction using the OPHDIAT database. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to analyze how diseases get worse over time. It uses special math equations called neural ordinary differential equations (NODE) and adds a “time-aware head” that helps the computer learn from patterns in the data. This approach is better than older methods because it takes into account when things happened, which is important for understanding disease progression. |
Keywords
* Artificial intelligence * Data augmentation * Latent space * Self supervised
