  Automated diagnosis of Alzheimer Disease(AD) from brain imaging, such as
magnetic resonance imaging (MRI), has become increasingly important and has
attracted the community to contribute many deep learning methods. However, many
of these methods are facing a trade-off that 3D models tend to be complicated
while 2D models cannot capture the full 3D intricacies from the data. In this
paper, we introduce a new model structure for diagnosing AD, and it can
complete with performances of 3D models while essentially is a 2D method (thus
computationally efficient). While the core idea lies in new perspective of
cutting the 3D images into multiple 2D slices from three dimensions, we
introduce multiple components that can further benefit the model in this new
perspective, including adaptively selecting the number of sclices in each
dimension, and the new attention mechanism. In addition, we also introduce a
morphology augmentation, which also barely introduces new computational loads,
but can help improve the diagnosis performances due to its alignment to the
pathology of AD. We name our method ADAPT, which stands for Alzheimer Diagnosis
through Adaptive Profiling Transformers. We test our model from a practical
perspective (the testing domains do not appear in the training one): the
diagnosis accuracy favors our ADAPT, while ADAPT uses less parameters than most
3D models use.
