ADAPT: Alzheimer's Diagnosis through Adaptive Profiling Transformers
Keywords: Deep Learning for healthcare, Alzheimer's Diagnosis
Abstract: Automated diagnosis of Alzheimer’s 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 inefficient
in training and inferencing 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 3D model’s performances while essentially is a 2D method
(thus computationally efficient). While the core idea lies in building different blocks
on different views according to physicians’ diagnosing perspectives, 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’s 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 with 4.5% improvement, while ADAPT uses at leat 14% less parameters
than the state-of-the-art models.
Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)
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Submission Number: 5507
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