Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: AI in Osteoporosis   In this chapter we explore and evaluate methods for trabecular bone
characterization and osteoporosis diagnosis with increased interest in sparse
approximations. We first describe texture representation and classification
techniques, patch-based methods such as Bag of Keypoints, and more recent deep
neural networks. Then we introduce the concept of sparse representations for
pattern recognition and we detail integrative sparse analysis methods and
classifier decision fusion methods. We report cross-validation results on
osteoporosis datasets of bone radiographs and compare the results produced by
the different categories of methods. We conclude that advances in the AI and
machine learning fields have enabled the development of methods that can be
used as diagnostic tools in clinical settings.

30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
16
The categories of this paper are: stat
0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
20
The categories of this paper are: acc-phys
34
The categories of this paper are: chem-ph