Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Trends in deep learning for medical hyperspectral image analysis   Deep learning algorithms have seen acute growth of interest in their
applications throughout several fields of interest in the last decade, with
medical hyperspectral imaging being a particularly promising domain. So far, to
the best of our knowledge, there is no review paper that discusses the
implementation of deep learning for medical hyperspectral imaging, which is
what this review paper aims to accomplish by examining publications that
currently utilize deep learning to perform effective analysis of medical
hyperspectral imagery. This paper discusses deep learning concepts that are
relevant and applicable to medical hyperspectral imaging analysis, several of
which have been implemented since the boom in deep learning. This will comprise
of reviewing the use of deep learning for classification, segmentation, and
detection in order to investigate the analysis of medical hyperspectral
imaging. Lastly, we discuss the current and future challenges pertaining to
this discipline and the possible efforts to overcome such trials.

33
The categories of this paper are: plasm-ph
31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
29
The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
24
The categories of this paper are: dg-ga
30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat
2
The categories of this paper are: math
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph