Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Uncovering convolutional neural network decisions for diagnosing
  multiple sclerosis on conventional MRI using layer-wise relevance propagation   Machine learning-based imaging diagnostics has recently reached or even
superseded the level of clinical experts in several clinical domains. However,
classification decisions of a trained machine learning system are typically
non-transparent, a major hindrance for clinical integration, error tracking or
knowledge discovery. In this study, we present a transparent deep learning
framework relying on convolutional neural networks (CNNs) and layer-wise
relevance propagation (LRP) for diagnosing multiple sclerosis (MS). MS is
commonly diagnosed utilizing a combination of clinical presentation and
conventional magnetic resonance imaging (MRI), specifically the occurrence and
presentation of white matter lesions in T2-weighted images. We hypothesized
that using LRP in a naive predictive model would enable us to uncover relevant
image features that a trained CNN uses for decision-making. Since imaging
markers in MS are well-established this would enable us to validate the
respective CNN model. First, we pre-trained a CNN on MRI data from the
Alzheimer's Disease Neuroimaging Initiative (n = 921), afterwards specializing
the CNN to discriminate between MS patients and healthy controls (n = 147).
Using LRP, we then produced a heatmap for each subject in the holdout set
depicting the voxel-wise relevance for a particular classification decision.
The resulting CNN model resulted in a balanced accuracy of 87.04% and an area
under the curve of 96.08% in a receiver operating characteristic curve. The
subsequent LRP visualization revealed that the CNN model focuses indeed on
individual lesions, but also incorporates additional information such as lesion
location, non-lesional white matter or gray matter areas such as the thalamus,
which are established conventional and advanced MRI markers in MS. We conclude
that LRP and the proposed framework have the capability to make diagnostic
decisions of...

26
The categories of this paper are: bayes-an
29
The categories of this paper are: funct-an
18
The categories of this paper are: econ
36
The categories of this paper are: cmp-lg
23
The categories of this paper are: solv-int
10
The categories of this paper are: q-bio
2
The categories of this paper are: math
5
The categories of this paper are: gr-qc
21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph
35
The categories of this paper are: mtrl-th
37
The categories of this paper are: supr-con
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
13
The categories of this paper are: quant-ph