Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Synthesizing Filamentary Structured Images with GANs   This paper aims at synthesizing filamentary structured images such as retinal
fundus images and neuronal images, as follows: Given a ground-truth, to
generate multiple realistic looking phantoms. A ground-truth could be a binary
segmentation map containing the filamentary structured morphology, while the
synthesized output image is of the same size as the ground-truth and has
similar visual appearance to what have been presented in the training set. Our
approach is inspired by the recent progresses in generative adversarial nets
(GANs) as well as image style transfer. In particular, it is dedicated to our
problem context with the following properties: Rather than large-scale dataset,
it works well in the presence of as few as 10 training examples, which is
common in medical image analysis; It is capable of synthesizing diverse images
from the same ground-truth; Last and importantly, the synthetic images produced
by our approach are demonstrated to be useful in boosting image analysis
performance. Empirical examination over various benchmarks of fundus and
neuronal images demonstrate the advantages of the proposed approach.

17
The categories of this paper are: q-fin
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph
16
The categories of this paper are: stat
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
3
The categories of this paper are: physics
13
The categories of this paper are: quant-ph
18
The categories of this paper are: econ
29
The categories of this paper are: funct-an
20
The categories of this paper are: acc-phys
14
The categories of this paper are: math-ph
0
The categories of this paper are: hep-ph
5
The categories of this paper are: gr-qc