Pulmonary nodule detection using improved faster R-CNN and 3D Resnet

Rong Fan*, Sei Ichiro Kamata, Yawen Chen

*この研究の対応する著者

研究成果: Conference contribution

抄録

Pulmonary nodule detection system consists of two steps: candidate detection and false positive reduction. To dynamically adapt the sizes and ratios of the nodules, Local Density based Iterative Self-Organizing Data Analysis Techniques Algorithm (D-ISODATA) is proposed for automated anchor boxes configuration. For candidate detection, instead of fixed anchor, D-ISODATA is utilized for automatically generate anchors to adapt to high variability of nodules. D-ISODATA initializes clustering center and removes noises based on the principle of maximum local density and further clustering is carried out with self-adaptability. In addition, attention mechanism is adopted in feature channels to enable the model to focus on nodule-related features. For false positive reduction, 3D Resnet is utilized to extract the three-dimensional features of nodules. Experiments are carried out on LUNA16 dataset and show out a sensitivity of 93.6% with 0.15 false positive per scan. The results show preferable performance of the proposed method.

本文言語English
ホスト出版物のタイトルThirteenth International Conference on Digital Image Processing, ICDIP 2021
編集者Xudong Jiang, Hiroshi Fujita
出版社SPIE
ISBN(電子版)9781510646001
DOI
出版ステータスPublished - 2021
イベント13th International Conference on Digital Image Processing, ICDIP 2021 - Singapore, Singapore
継続期間: 2021 5 202021 5 23

出版物シリーズ

名前Proceedings of SPIE - The International Society for Optical Engineering
11878
ISSN(印刷版)0277-786X
ISSN(電子版)1996-756X

Conference

Conference13th International Conference on Digital Image Processing, ICDIP 2021
国/地域Singapore
CitySingapore
Period21/5/2021/5/23

ASJC Scopus subject areas

  • 電子材料、光学材料、および磁性材料
  • 凝縮系物理学
  • コンピュータ サイエンスの応用
  • 応用数学
  • 電子工学および電気工学

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