Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: TUNet: Incorporating segmentation maps to improve classification   Determining the localization of specific protein in human cells is important
for understanding cellular functions and biological processes of underlying
diseases. Among imaging techniques, high-throughput fluorescence microscopy
imaging is an efficient biotechnology to stain the protein of interest in a
cell. In this work, we present a novel classification model Twin U-Net (TUNet)
for processing and classifying the belonging of protein in the Atlas images.
Several notable Deep Learning models including GoogleNet and Resnet have been
employed for comparison. Results have shown that our system obtaining
competitive performance.

22
The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
6
The categories of this paper are: astro-ph
21
The categories of this paper are: adap-org
10
The categories of this paper are: q-bio
15
The categories of this paper are: nucl-ex
31
The categories of this paper are: ao-sci
7
The categories of this paper are: hep-th
24
The categories of this paper are: dg-ga
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph