Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Uncertainty-Aware Semi-Supervised Method Using Large Unlabeled and
  Limited Labeled COVID-19 Data   The new coronavirus has caused more than one million deaths and continues to
spread rapidly. This virus targets the lungs, causing respiratory distress
which can be mild or severe. The X-ray or computed tomography (CT) images of
lungs can reveal whether the patient is infected with COVID-19 or not. Many
researchers are trying to improve COVID-19 detection using artificial
intelligence. Our motivation is to develop an automatic method that can cope
with scenarios in which preparing labeled data is time consuming or expensive.
In this article, we propose a Semi-supervised Classification using Limited
Labeled Data (SCLLD) relying on Sobel edge detection and Generative Adversarial
Networks (GANs) to automate the COVID-19 diagnosis. The GAN discriminator
output is a probabilistic value which is used for classification in this work.
The proposed system is trained using 10,000 CT scans collected from Omid
Hospital, whereas a public dataset is also used for validating our system. The
proposed method is compared with other state-of-the-art supervised methods such
as Gaussian processes. To the best of our knowledge, this is the first time a
semi-supervised method for COVID-19 detection is presented. Our system is
capable of learning from a mixture of limited labeled and unlabeled data where
supervised learners fail due to a lack of sufficient amount of labeled data.
Thus, our semi-supervised training method significantly outperforms the
supervised training of Convolutional Neural Network (CNN) when labeled training
data is scarce. The 95% confidence intervals for our method in terms of
accuracy, sensitivity, and specificity are 99.56 +- 0.20%, 99.88 +- 0.24%, and
99.40 +- 0.18%, respectively, whereas intervals for the CNN (trained
supervised) are 68.34 +- 4.11%, 91.2 +- 6.15%, and 46.40 +- 5.21%.

9
The categories of this paper are: nlin
28
The categories of this paper are: alg-geom
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
22
The categories of this paper are: chao-dyn
25
The categories of this paper are: patt-sol
10
The categories of this paper are: q-bio
18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an
2
The categories of this paper are: math
27
The categories of this paper are: comp-gas