Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Detection and Attention: Diagnosing Pulmonary Lung Cancer from CT by
  Imitating Physicians   This paper proposes a novel and efficient method to build a Computer-Aided
Diagnoses (CAD) system for lung nodule detection based on Computed Tomography
(CT). This task was treated as an Object Detection on Video (VID) problem by
imitating how a radiologist reads CT scans. A lung nodule detector was trained
to automatically learn nodule features from still images to detect lung nodule
candidates with both high recall and accuracy. Unlike previous work which used
3-dimensional information around the nodule to reduce false positives, we
propose two simple but efficient methods, Multi-slice propagation (MSP) and
Motionless-guide suppression (MLGS), which analyze sequence information of CT
scans to reduce false negatives and suppress false positives. We evaluated our
method in open-source LUNA16 dataset which contains 888 CT scans, and obtained
state-of-the-art result (Free-Response Receiver Operating Characteristic score
of 0.892) with detection speed (end to end within 20 seconds per patient on a
single NVidia GTX 1080) much higher than existing methods.

9
The categories of this paper are: nlin
16
The categories of this paper are: stat
22
The categories of this paper are: chao-dyn
8
The categories of this paper are: hep-ex
12
The categories of this paper are: nucl-th
37
The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
15
The categories of this paper are: nucl-ex
26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat
29
The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess
31
The categories of this paper are: ao-sci