Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 3D Convolutional Neural Networks for Classification of Functional
  Connectomes   Resting-state functional MRI (rs-fMRI) scans hold the potential to serve as a
diagnostic or prognostic tool for a wide variety of conditions, such as autism,
Alzheimer's disease, and stroke. While a growing number of studies have
demonstrated the promise of machine learning algorithms for rs-fMRI based
clinical or behavioral prediction, most prior models have been limited in their
capacity to exploit the richness of the data. For example, classification
techniques applied to rs-fMRI often rely on region-based summary statistics
and/or linear models. In this work, we propose a novel volumetric Convolutional
Neural Network (CNN) framework that takes advantage of the full-resolution 3D
spatial structure of rs-fMRI data and fits non-linear predictive models. We
showcase our approach on a challenging large-scale dataset (ABIDE, with N >
2,000) and report state-of-the-art accuracy results on rs-fMRI-based
discrimination of autism patients and healthy controls.

7
The categories of this paper are: hep-th
2
The categories of this paper are: math
18
The categories of this paper are: econ
6
The categories of this paper are: astro-ph
33
The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg
19
The categories of this paper are: eess
22
The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
3
The categories of this paper are: physics
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
17
The categories of this paper are: q-fin
14
The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th