Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Deep Learning Framework for Nuclear Segmentation and Classification in
  Histopathological Images   Nucleus segmentation and classification are the prerequisites in the workflow
of digital pathology processing. However, it is very challenging due to its
high-level heterogeneity and wide variations. This work proposes a deep neural
network to simultaneously achieve nuclear classification and segmentation,
which is designed using a unified framework with three different branches,
including segmentation, HoVer mapping, and classification. The segmentation
branch aims to generate the boundaries of each nucleus. The HoVer branch
calculates the horizontal and vertical distances of nuclear pixels to their
centres of mass. The nuclear classification branch is used to distinguish the
class of pixels inside the nucleus obtained from segmentation.

3
The categories of this paper are: physics
8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
16
The categories of this paper are: stat
28
The categories of this paper are: alg-geom
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The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
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The categories of this paper are: nucl-th
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The categories of this paper are: cond-mat
17
The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph
30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
2
The categories of this paper are: math