Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Feature Learning to Automatically Assess Radiographic Knee
  Osteoarthritis Severity   This chapter presents the investigations and the results of feature learning
using convolutional neural networks to automatically assess knee osteoarthritis
(OA) severity and the associated clinical and diagnostic features of knee OA
from X-ray images. Also, this chapter demonstrates that feature learning in a
supervised manner is more effective than using conventional handcrafted
features for automatic detection of knee joints and fine-grained knee OA image
classification. In the general machine learning approach to automatically
assess knee OA severity, the first step is to localize the region of interest
that is to detect and extract the knee joint regions from the radiographs, and
the next step is to classify the localized knee joints based on a radiographic
classification scheme such as Kellgren and Lawrence grades. First, the existing
approaches for detecting (or localizing) the knee joint regions based on
handcrafted features are reviewed and outlined. Next, three new approaches are
introduced: 1) to automatically detect the knee joint region using a fully
convolutional network, 2) to automatically assess the radiographic knee OA
using CNNs trained from scratch for classification and regression of knee joint
images to predict KL grades in ordinal and continuous scales, and 3) to
quantify the knee OA severity optimizing a weighted ratio of two loss
functions: categorical cross entropy and mean-squared error using
multi-objective convolutional learning and ordinal regression. Two public
datasets: the OAI and the MOST are used to evaluate the approaches with
promising results that outperform existing approaches. In summary, this work
primarily contributes to the field of automated methods for localization
(automatic detection) and quantification (image classification) of radiographic
knee OA.

28
The categories of this paper are: alg-geom
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th
19
The categories of this paper are: eess
2
The categories of this paper are: math
3
The categories of this paper are: physics
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
17
The categories of this paper are: q-fin
27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con