Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Learning in Computer-Aided Diagnosis and Treatment of Tumors: A
  Survey   Computer-Aided Diagnosis and Treatment of Tumors is a hot topic of deep
learning in recent years, which constitutes a series of medical tasks, such as
detection of tumor markers, the outline of tumor leisures, subtypes and stages
of tumors, prediction of therapeutic effect, and drug development. Meanwhile,
there are some deep learning models with precise positioning and excellent
performance produced in mainstream task scenarios. Thus follow to introduce
deep learning methods from task-orient, mainly focus on the improvements for
medical tasks. Then to summarize the recent progress in four stages of tumor
diagnosis and treatment, which named In-Vitro Diagnosis (IVD), Imaging
Diagnosis (ID), Pathological Diagnosis (PD), and Treatment Planning (TP).
According to the specific data types and medical tasks of each stage, we
present the applications of deep learning in the Computer-Aided Diagnosis and
Treatment of Tumors and analyzing the excellent works therein. This survey
concludes by discussing research issues and suggesting challenges for future
improvement.

35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
22
The categories of this paper are: chao-dyn
14
The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
15
The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int
21
The categories of this paper are: adap-org
16
The categories of this paper are: stat
20
The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph