Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Ridge Regression Neural Network for Pediatric Bone Age Assessment   Bone age is an important measure for assessing the skeletal and biological
maturity of children. Delayed or increased bone age is a serious concern for
pediatricians, and needs to be accurately assessed in a bid to determine
whether bone maturity is occurring at a rate consistent with chronological age.
In this paper, we introduce a unified deep learning framework for bone age
assessment using instance segmentation and ridge regression. The proposed
approach consists of two integrated stages. In the first stage, we employ an
image annotation and segmentation model to annotate and segment the hand from
the radiographic image, followed by background removal. In the second stage, we
design a regression neural network architecture composed of a pre-trained
convolutional neural network for learning salient features from the segmented
pediatric hand radiographs and a ridge regression output layer for predicting
the bone age. Experimental evaluation on a dataset of hand radiographs
demonstrates the competitive performance of our approach in comparison with
existing deep learning based methods for bone age assessment.

19
The categories of this paper are: eess
8
The categories of this paper are: hep-ex
11
The categories of this paper are: hep-lat
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an
15
The categories of this paper are: nucl-ex
31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an