Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Interpretable Graph Convolutional Network of Multi-Modality Brain
  Imaging for Alzheimer's Disease Diagnosis   Identification of brain regions related to the specific neurological
disorders are of great importance for biomarker and diagnostic studies. In this
paper, we propose an interpretable Graph Convolutional Network (GCN) framework
for the identification and classification of Alzheimer's disease (AD) using
multi-modality brain imaging data. Specifically, we extended the Gradient Class
Activation Mapping (Grad-CAM) technique to quantify the most discriminative
features identified by GCN from brain connectivity patterns. We then utilized
them to find signature regions of interest (ROIs) by detecting the difference
of features between regions in healthy control (HC), mild cognitive impairment
(MCI), and AD groups. We conducted the experiments on the ADNI database with
imaging data from three modalities, including VBM-MRI, FDG-PET, and AV45-PET,
and showed that the ROI features learned by our method were effective for
enhancing the performances of both clinical score prediction and disease status
identification. It also successfully identified biomarkers associated with AD
and MCI.

17
The categories of this paper are: q-fin
18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an
7
The categories of this paper are: hep-th
35
The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio
16
The categories of this paper are: stat
30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
3
The categories of this paper are: physics
15
The categories of this paper are: nucl-ex
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph
2
The categories of this paper are: math