Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised learning of the brain connectivity dynamic using residual
  D-net   In this paper, we propose a novel unsupervised learning method to learn the
brain dynamics using a deep learning architecture named residual D-net. As it
is often the case in medical research, in contrast to typical deep learning
tasks, the size of the resting-state functional Magnetic Resonance Image
(rs-fMRI) datasets for training is limited. Thus, the available data should be
very efficiently used to learn the complex patterns underneath the brain
connectivity dynamics. To address this issue, we use residual connections to
alleviate the training complexity through recurrent multi-scale representation.
We conduct two classification tasks to differentiate early and late stage Mild
Cognitive Impairment (MCI) from Normal healthy Control (NC) subjects. The
experiments verify that our proposed residual D-net indeed learns the brain
connectivity dynamics, leading to significantly higher classification accuracy
compared to previously published techniques.

0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
36
The categories of this paper are: cmp-lg
20
The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an
22
The categories of this paper are: chao-dyn
17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
27
The categories of this paper are: comp-gas