Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Image analysis for Alzheimer's disease prediction: Embracing
  pathological hallmarks for model architecture design   Alzheimer's disease (AD) is associated with local (e.g. brain tissue atrophy)
and global brain changes (loss of cerebral connectivity), which can be detected
by high-resolution structural magnetic resonance imaging. Conventionally, these
changes and their relation to AD are investigated independently. Here, we
introduce a novel, highly-scalable approach that simultaneously captures
$\textit{local}$ and $\textit{global}$ changes in the diseased brain. It is
based on a neural network architecture that combines patch-based,
high-resolution 3D-CNNs with global topological features, evaluating
multi-scale brain tissue connectivity. Our local-global approach reached
competitive results with an average precision score of $0.95\pm0.03$ for the
classification of cognitively normal subjects and AD patients (prevalence
$\approx 55\%$).

20
The categories of this paper are: acc-phys
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
30
The categories of this paper are: q-alg
26
The categories of this paper are: bayes-an
22
The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph
2
The categories of this paper are: math
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
13
The categories of this paper are: quant-ph
32
The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int
28
The categories of this paper are: alg-geom