Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving the Interpretability of fMRI Decoding using Deep Neural
  Networks and Adversarial Robustness   Deep neural networks (DNNs) are being increasingly used to make predictions
from functional magnetic resonance imaging (fMRI) data. However, they are
widely seen as uninterpretable "black boxes", as it can be difficult to
discover what input information is used by the DNN in the process, something
important in both cognitive neuroscience and clinical applications. A saliency
map is a common approach for producing interpretable visualizations of the
relative importance of input features for a prediction. However, methods for
creating maps often fail due to DNNs being sensitive to input noise, or by
focusing too much on the input and too little on the model. It is also
challenging to evaluate how well saliency maps correspond to the truly relevant
input information, as ground truth is not always available. In this paper, we
review a variety of methods for producing gradient-based saliency maps, and
present a new adversarial training method we developed to make DNNs robust to
input noise, with the goal of improving interpretability. We introduce two
quantitative evaluation procedures for saliency map methods in fMRI, applicable
whenever a DNN or linear model is being trained to decode some information from
imaging data. We evaluate the procedures using a synthetic dataset where the
complex activation structure is known, and on saliency maps produced for DNN
and linear models for task decoding in the Human Connectome Project (HCP)
dataset. Our key finding is that saliency maps produced with different methods
vary widely in interpretability, in both in synthetic and HCP fMRI data.
Strikingly, even when DNN and linear models decode at comparable levels of
performance, DNN saliency maps score higher on interpretability than linear
model saliency maps (derived via weights or gradient). Finally, saliency maps
produced with our adversarial training method outperform those from other
methods.

26
The categories of this paper are: bayes-an
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
21
The categories of this paper are: adap-org
22
The categories of this paper are: chao-dyn
25
The categories of this paper are: patt-sol
27
The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
35
The categories of this paper are: mtrl-th
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The categories of this paper are: eess
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-lat
13
The categories of this paper are: quant-ph