  Modeling temporal changes in subcortical structures is crucial for a better
understanding of the progression of Alzheimer's disease (AD). Given their
flexibility to adapt to heterogeneous sequence lengths, mesh-based transformer
architectures have been proposed in the past for predicting hippocampus
deformations across time. However, one of the main limitations of transformers
is the large amount of trainable parameters, which makes the application on
small datasets very challenging. In addition, current methods do not include
relevant non-image information that can help to identify AD-related patterns in
the progression. To this end, we introduce CASHformer, a transformer-based
framework to model longitudinal shape trajectories in AD. CASHformer
incorporates the idea of pre-trained transformers as universal compute engines
that generalize across a wide range of tasks by freezing most layers during
fine-tuning. This reduces the number of parameters by over 90% with respect to
the original model and therefore enables the application of large models on
small datasets without overfitting. In addition, CASHformer models cognitive
decline to reveal AD atrophy patterns in the temporal sequence. Our results
show that CASHformer reduces the reconstruction error by 73% compared to
previously proposed methods. Moreover, the accuracy of detecting patients
progressing to AD increases by 3% with imputing missing longitudinal shape
data.
