Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Structured learning for mass segmentation from Mammograms   In this paper, we present a novel method for the segmentation of breast
masses from mammograms exploring structured and deep learning. Specifically,
using structured support vector machine (SSVM), we formulate a model that
combines different types of potential functions, including one that classifies
image regions using deep learning. Our main goal with this work is to show the
accuracy and efficiency improvements that these relatively new techniques can
provide for the segmentation of breast masses from mammograms. We also propose
an easily reproducible quantitative analysis to as- sess the performance of
breast mass segmentation methodologies based on widely accepted accuracy and
running time measurements on public datasets, which will facilitate further
comparisons for this segmentation problem. In particular, we use two publicly
available datasets (DDSM-BCRP and INbreast) and propose the computa- tion of
the running time taken for the methodology to produce a mass segmentation given
an input image and the use of the Dice index to quantitatively measure the
segmentation accuracy. For both databases, we show that our proposed
methodology produces competitive results in terms of accuracy and running time.

23
The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
4
The categories of this paper are: cond-mat
14
The categories of this paper are: math-ph
0
The categories of this paper are: hep-ph
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga
19
The categories of this paper are: eess
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
15
The categories of this paper are: nucl-ex
28
The categories of this paper are: alg-geom