Name
Abstract | ||
|---|---|---|
Brain aging is a widely studied longitudinal process throughout which the brain undergoes considerable morphological changes and various machine learning approaches have been proposed to analyze it. Within this context, brain age prediction from structural MR images and age-specific brain morphology template generation are two problems that have attracted much attention. While most approaches tackle these tasks independently, we assume that they are inverse directions of the same functional bidirectional relationship between a brain's morphology and an age variable. In this paper, we propose to model this relationship with a single conditional normalizing flow, which unifies brain age prediction and age-conditioned generative modeling in a novel way. In an initial evaluation of this idea, we show that our normalizing flow brain aging model can accurately predict brain age while also being able to generate age-specific brain morphology templates that realistically represent the typical aging trend in a healthy population. This work is a step towards unified modeling of functional relationships between 3D brain morphology and clinical variables of interest with powerful normalizing flows. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/978-3-030-66843-3_3 | MLCN/RNO-AI@MICCAI |
DocType | Citations | PageRank |
Conference | 2 | 0.37 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matthias Wilms | 1 | 2 | 2.06 |
| Jordan J. Bannister | 2 | 2 | 0.37 |
| Pauline Mouches | 3 | 4 | 1.75 |
| M Ethan MacDonald | 4 | 4 | 1.43 |
| Deepthi Rajashekar | 5 | 3 | 1.40 |
| Sönke Langner | 6 | 3 | 1.73 |
| Nils Daniel Forkert | 7 | 26 | 15.30 |