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Proceedings Paper

False positive reduction in lymph node detection by using convolutional neural network with multi-view input
Author(s): Jiaqi Wang; Li Xu
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Paper Abstract

The presence of enlarged lymph nodes is a signal of malignant disease or infection. Lymph nodes detection plays an important role in clinical diagnostic tasks. Previous lymph nodes detection methods achieve high sensitivity at the cost of a high false positive rate. In this paper, we propose a method that helps reject false positives. Features are extracted separately from 2D CT slices by using a deep convolutional neural network with multi-view input. Separated feature layers can extract the most suitable features from each input slice individually. We validate the approach on a public dataset and improve the sensitivity by reducing the false positive rate.

Paper Details

Date Published: 14 February 2020
PDF: 6 pages
Proc. SPIE 11431, MIPPR 2019: Parallel Processing of Images and Optimization Techniques; and Medical Imaging, 1143109 (14 February 2020); doi: 10.1117/12.2535551
Show Author Affiliations
Jiaqi Wang, Zhejiang Univ. (China)
Li Xu, Zhejiang Univ. (China)


Published in SPIE Proceedings Vol. 11431:
MIPPR 2019: Parallel Processing of Images and Optimization Techniques; and Medical Imaging
Hong Sun; Bruce Hirsch; Chao Cai, Editor(s)

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