Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Machine Learning Methods for Histopathological Image Analysis: A Review   Histopathological images (HIs) are the gold standard for evaluating some
types of tumors for cancer diagnosis. The analysis of such images is not only
time and resource consuming, but also very challenging even for experienced
pathologists, resulting in inter- and intra-observer disagreements. One of the
ways of accelerating such an analysis is to use computer-aided diagnosis (CAD)
systems. In this paper, we present a review on machine learning methods for
histopathological image analysis, including shallow and deep learning methods.
We also cover the most common tasks in HI analysis, such as segmentation and
feature extraction. In addition, we present a list of publicly available and
private datasets that have been used in HI research.

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The categories of this paper are: q-bio
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The categories of this paper are: supr-con
7
The categories of this paper are: hep-th
18
The categories of this paper are: econ
17
The categories of this paper are: q-fin
11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph
34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
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The categories of this paper are: math-ph
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The categories of this paper are: stat
22
The categories of this paper are: chao-dyn
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The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th