Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Self-supervised learning methods and applications in medical imaging
  analysis: A survey   The availability of high quality annotated medical imaging datasets is a
major problem that collides with machine learning applications in the field of
medical imaging analysis and impedes its advancement. Self-supervised learning
is a recent training paradigm that enables learning robust representations
without the need for human annotation which can be considered as an effective
solution for the scarcity in annotated medical data. This article reviews the
state-of-the-art research directions in self-supervised learning approaches for
image data with concentration on their applications in the field of medical
imaging analysis. The article covers a set of the most recent self-supervised
learning methods from the computer vision field as they are applicable to the
medical imaging analysis and categorize them as predictive, generative and
contrastive approaches. Moreover, the article covers (40) of the most recent
researches in the field of self-supervised learning in medical imaging analysis
aiming at shedding the light on the recent innovation in the field. Ultimately,
the article concludes with possible future research directions in the field.

3
The categories of this paper are: physics
23
The categories of this paper are: solv-int
4
The categories of this paper are: cond-mat
5
The categories of this paper are: gr-qc
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
17
The categories of this paper are: q-fin
14
The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th
28
The categories of this paper are: alg-geom
36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th