Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DeepAtrophy: Teaching a Neural Network to Differentiate Progressive
  Changes from Noise on Longitudinal MRI in Alzheimer's Disease   Volume change measures derived from longitudinal MRI (e.g. hippocampal
atrophy) are a well-studied biomarker of disease progression in Alzheimer's
Disease (AD) and are used in clinical trials to track the therapeutic efficacy
of disease-modifying treatments. However, longitudinal MRI change measures can
be confounded by non-biological factors, such as different degrees of head
motion and susceptibility artifact between pairs of MRI scans. We hypothesize
that deep learning methods applied directly to pairs of longitudinal MRI scans
can be trained to differentiate between biological changes and non-biological
factors better than conventional approaches based on deformable image
registration. To achieve this, we make a simplifying assumption that biological
factors are associated with time (i.e. the hippocampus shrinks overtime in the
aging population) whereas non-biological factors are independent of time. We
then formulate deep learning networks to infer the temporal order of
same-subject MRI scans input to the network in arbitrary order; as well as to
infer ratios between interscan intervals for two pairs of same-subject MRI
scans. In the test dataset, these networks perform better in tasks of temporal
ordering (89.3%) and interscan interval inference (86.1%) than a
state-of-the-art deformation-based morphometry method ALOHA (76.6% and 76.1%
respectively) (Das et al., 2012). Furthermore, we derive a disease progression
score from the network that is able to detect a group difference between 58
preclinical AD and 75 beta-amyloid-negative cognitively normal individuals
within one year, compared to two years for ALOHA. This suggests that deep
learning can be trained to differentiate MRI changes due to biological factors
(tissue loss) from changes due to non-biological factors, leading to novel
biomarkers that are more sensitive to longitudinal changes at the earliest
stages of AD.

31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an
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The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
9
The categories of this paper are: nlin
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The categories of this paper are: hep-lat
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The categories of this paper are: math-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: comp-gas
2
The categories of this paper are: math
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The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
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The categories of this paper are: alg-geom