Abstract
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.
Publication
Medical Image Computing and Computer-Assisted Intervention