Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Informative sample generation using class aware generative adversarial
  networks for classification of chest Xrays   Training robust deep learning (DL) systems for disease detection from medical
images is challenging due to limited images covering different disease types
and severity. The problem is especially acute, where there is a severe class
imbalance. We propose an active learning (AL) framework to select most
informative samples for training our model using a Bayesian neural network.
Informative samples are then used within a novel class aware generative
adversarial network (CAGAN) to generate realistic chest xray images for data
augmentation by transferring characteristics from one class label to another.
Experiments show our proposed AL framework is able to achieve state-of-the-art
performance by using about $35\%$ of the full dataset, thus saving significant
time and effort over conventional methods.

9
The categories of this paper are: nlin
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat
16
The categories of this paper are: stat
13
The categories of this paper are: quant-ph
10
The categories of this paper are: q-bio
20
The categories of this paper are: acc-phys
28
The categories of this paper are: alg-geom
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
30
The categories of this paper are: q-alg
26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
3
The categories of this paper are: physics