Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Patch-based Generative Adversarial Network Towards Retinal Vessel
  Segmentation   Retinal blood vessels are considered to be the reliable diagnostic biomarkers
of ophthalmologic and diabetic retinopathy. Monitoring and diagnosis totally
depends on expert analysis of both thin and thick retinal vessels which has
recently been carried out by various artificial intelligent techniques.
Existing deep learning methods attempt to segment retinal vessels using a
unified loss function optimized for both thin and thick vessels with equal
importance. Due to variable thickness, biased distribution, and difference in
spatial features of thin and thick vessels, unified loss function are more
influential towards identification of thick vessels resulting in weak
segmentation. To address this problem, a conditional patch-based generative
adversarial network is proposed which utilizes a generator network and a
patch-based discriminator network conditioned on the sample data with an
additional loss function to learn both thin and thick vessels. Experiments are
conducted on publicly available STARE and DRIVE datasets which show that the
proposed model outperforms the state-of-the-art methods.

37
The categories of this paper are: supr-con
15
The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin
7
The categories of this paper are: hep-th
6
The categories of this paper are: astro-ph
22
The categories of this paper are: chao-dyn
16
The categories of this paper are: stat
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph
24
The categories of this paper are: dg-ga
27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org