Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generic Feature Learning for Wireless Capsule Endoscopy Analysis   The interpretation and analysis of the wireless capsule endoscopy recording
is a complex task which requires sophisticated computer aided decision (CAD)
systems in order to help physicians with the video screening and, finally, with
the diagnosis. Most of the CAD systems in the capsule endoscopy share a common
system design, but use very different image and video representations. As a
result, each time a new clinical application of WCE appears, new CAD system has
to be designed from scratch. This characteristic makes the design of new CAD
systems a very time consuming. Therefore, in this paper we introduce a system
for small intestine motility characterization, based on Deep Convolutional
Neural Networks, which avoids the laborious step of designing specific features
for individual motility events. Experimental results show the superiority of
the learned features over alternative classifiers constructed by using state of
the art hand-crafted features. In particular, it reaches a mean classification
accuracy of 96% for six intestinal motility events, outperforming the other
classifiers by a large margin (a 14% relative performance increase).

32
The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg
3
The categories of this paper are: physics
10
The categories of this paper are: q-bio
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
19
The categories of this paper are: eess
15
The categories of this paper are: nucl-ex
11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn
7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
17
The categories of this paper are: q-fin
12
The categories of this paper are: nucl-th