Spherical Detection Frame and Attention-Based Pulmonary Nodule Detection

Qiuyu Dong, Huijuan Lu, Renfeng Wang, Zhendong Ming, Wenjie Zhu, Yudong Yao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Lung cancer is one of the cancers with the highest incidence and mortality in the world, and early detection of lung nodules can effectively reduce the mortality rate of lung cancer. At present, most of the lung nodule detection methods based on deep learning face the problem of low sensitivity or high false positive rate. This paper proposes a loss function suitable for the clinical features of lung nodules and introduces a novel Squeeze-and-Excitation module in the depth direction, which alleviates the problem of high false positive rate in the detection process and achieves good results. In this paper, the competition performance metric (CPM) on the LUNA16 dataset reached 88.16%.

Original languageEnglish
Title of host publicationProceedings - 2023 13th International Conference on Information Technology in Medicine and Education, ITME 2023
Pages740-744
Number of pages5
ISBN (Electronic)9798350319156
DOIs
StatePublished - 2023
Event13th International Conference on Information Technology in Medicine and Education, ITME 2023 - Wuyishan, China
Duration: 24 Nov 202326 Nov 2023

Publication series

NameProceedings - 2023 13th International Conference on Information Technology in Medicine and Education, ITME 2023

Conference

Conference13th International Conference on Information Technology in Medicine and Education, ITME 2023
Country/TerritoryChina
CityWuyishan
Period24/11/2326/11/23

Keywords

  • Attention mechanisms
  • Feature pyramids
  • Pulmonary nodule detection
  • Spherical detection frame

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