Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep learning for photoacoustic imaging: a survey   Machine learning has been developed dramatically and witnessed a lot of
applications in various fields over the past few years. This boom originated in
2009, when a new model emerged, that is, the deep artificial neural network,
which began to surpass other established mature models on some important
benchmarks. Later, it was widely used in academia and industry. Ranging from
image analysis to natural language processing, it fully exerted its magic and
now become the state-of-the-art machine learning models. Deep neural networks
have great potential in medical imaging technology, medical data analysis,
medical diagnosis and other healthcare issues, and is promoted in both
pre-clinical and even clinical stages. In this review, we performed an overview
of some new developments and challenges in the application of machine learning
to medical image analysis, with a special focus on deep learning in
photoacoustic imaging. The aim of this review is threefold: (i) introducing
deep learning with some important basics, (ii) reviewing recent works that
apply deep learning in the entire ecological chain of photoacoustic imaging,
from image reconstruction to disease diagnosis, (iii) providing some open
source materials and other resources for researchers interested in applying
deep learning to photoacoustic imaging.

11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph
23
The categories of this paper are: solv-int
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
18
The categories of this paper are: econ
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
35
The categories of this paper are: mtrl-th
22
The categories of this paper are: chao-dyn
10
The categories of this paper are: q-bio
26
The categories of this paper are: bayes-an
12
The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th