Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Weakly Supervised Cell Instance Segmentation by Propagating from
  Detection Response   Cell shape analysis is important in biomedical research. Deep learning
methods may perform to segment individual cells if they use sufficient training
data that the boundary of each cell is annotated. However, it is very
time-consuming for preparing such detailed annotation for many cell culture
conditions. In this paper, we propose a weakly supervised method that can
segment individual cell regions who touch each other with unclear boundaries in
dense conditions without the training data for cell regions. We demonstrated
the efficacy of our method using several data-set including multiple cell types
captured by several types of microscopy. Our method achieved the highest
accuracy compared with several conventional methods. In addition, we
demonstrated that our method can perform without any annotation by using
fluorescence images that cell nuclear were stained as training data. Code is
publicly available in "https://github.com/naivete5656/WSISPDR".

2
The categories of this paper are: math
27
The categories of this paper are: comp-gas
0
The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
24
The categories of this paper are: dg-ga
20
The categories of this paper are: acc-phys
12
The categories of this paper are: nucl-th
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
22
The categories of this paper are: chao-dyn
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
16
The categories of this paper are: stat
11
The categories of this paper are: hep-lat