Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Holistic Interstitial Lung Disease Detection using Deep Convolutional
  Neural Networks: Multi-label Learning and Unordered Pooling   Accurately predicting and detecting interstitial lung disease (ILD) patterns
given any computed tomography (CT) slice without any pre-processing
prerequisites, such as manually delineated regions of interest (ROIs), is a
clinically desirable, yet challenging goal. The majority of existing work
relies on manually-provided ILD ROIs to extract sampled 2D image patches from
CT slices and, from there, performs patch-based ILD categorization. Acquiring
manual ROIs is labor intensive and serves as a bottleneck towards
fully-automated CT imaging ILD screening over large-scale populations.
Furthermore, despite the considerable high frequency of more than one ILD
pattern on a single CT slice, previous works are only designed to detect one
ILD pattern per slice or patch.
  To tackle these two critical challenges, we present multi-label deep
convolutional neural networks (CNNs) for detecting ILDs from holistic CT slices
(instead of ROIs or sub-images). Conventional single-labeled CNN models can be
augmented to cope with the possible presence of multiple ILD pattern labels,
via 1) continuous-valued deep regression based robust norm loss functions or 2)
a categorical objective as the sum of element-wise binary logistic losses. Our
methods are evaluated and validated using a publicly available database of 658
patient CT scans under five-fold cross-validation, achieving promising
performance on detecting four major ILD patterns: Ground Glass, Reticular,
Honeycomb, and Emphysema. We also investigate the effectiveness of a CNN
activation-based deep-feature encoding scheme using Fisher vector encoding,
which treats ILD detection as spatially-unordered deep texture classification.

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The categories of this paper are: q-fin
7
The categories of this paper are: hep-th
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
15
The categories of this paper are: nucl-ex
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
30
The categories of this paper are: q-alg
21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
13
The categories of this paper are: quant-ph
14
The categories of this paper are: math-ph
16
The categories of this paper are: stat
26
The categories of this paper are: bayes-an
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The categories of this paper are: solv-int