@inproceedings{964dc9d81e1943498f6377ded8830062,
title = "Pulmonary nodule detection using improved faster R-CNN and 3D Resnet",
abstract = "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.",
keywords = "Attention mechanism, Computer-aided system, ISODATA, Local density, Pulmonary nodule detection",
author = "Rong Fan and Kamata, {Sei Ichiro} and Yawen Chen",
note = "Publisher Copyright: {\textcopyright} 2021 SPIE; 13th International Conference on Digital Image Processing, ICDIP 2021 ; Conference date: 20-05-2021 Through 23-05-2021",
year = "2021",
doi = "10.1117/12.2599884",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Xudong Jiang and Hiroshi Fujita",
booktitle = "Thirteenth International Conference on Digital Image Processing, ICDIP 2021",
}