Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Learning COVID-19 Features on CXR using Limited Training Data Sets   Under the global pandemic of COVID-19, the use of artificial intelligence to
analyze chest X-ray (CXR) image for COVID-19 diagnosis and patient triage is
becoming important. Unfortunately, due to the emergent nature of the COVID-19
pandemic, a systematic collection of the CXR data set for deep neural network
training is difficult. To address this problem, here we propose a patch-based
convolutional neural network approach with a relatively small number of
trainable parameters for COVID-19 diagnosis. The proposed method is inspired by
our statistical analysis of the potential imaging biomarkers of the CXR
radiographs. Experimental results show that our method achieves
state-of-the-art performance and provides clinically interpretable saliency
maps, which are useful for COVID-19 diagnosis and patient triage.

2
The categories of this paper are: math
24
The categories of this paper are: dg-ga
9
The categories of this paper are: nlin
10
The categories of this paper are: q-bio
21
The categories of this paper are: adap-org
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
7
The categories of this paper are: hep-th