Abstract
Lung cancer is a global and dangerous disease, and its early detection is crucial for reducing the risks of mortality. In this regard, it has been of great interest in developing a computer-aided system for pulmonary nodules detection as early as possible on thoracic CT scans. In general, a nodule detection system involves two steps: (i) candidate nodule detection at a high sensitivity, which captures many false positives and (ii) false positive reduction from candidates. However, due to the high variation of nodule morphological characteristics and the possibility of mistaking them for neighboring organs, candidate nodule detection remains a challenge. In this study, we propose a novel Multi-scale Gradual Integration Convolutional Neural Network (MGI-CNN), designed with three main strategies: (1) to use multi-scale inputs with different levels of contextual information, (2) to use abstract information inherent in different input scales with gradual integration, and (3) to learn multi-stream feature integration in an end-to-end manner. To verify the efficacy of the proposed network, we conducted exhaustive experiments on the LUNA16 challenge datasets by comparing the performance of the proposed method with state-of-the-art methods in the literature. On two candidate subsets of the LUNA16 dataset, i.e., V1 and V2, our method achieved an average CPM of 0.908 (V1) and 0.942 (V2), outperforming comparable methods by a large margin. Our MGI-CNN is implemented in Python using TensorFlow and the source code is available from https://github.com/ku-milab/MGICNN.
| Original language | English |
|---|---|
| Pages (from-to) | 1-10 |
| Number of pages | 10 |
| Journal | Neural Networks |
| Volume | 115 |
| DOIs | |
| Publication status | Published - 2019 Jul 1 |
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Keywords
- False positive reduction
- Lung cancer screening
- Multi-scale convolutional neural network
- Multi-stream feature integration
- Pulmonary nodule detection
ASJC Scopus subject areas
- Cognitive Neuroscience
- Artificial Intelligence
Cite this
Multi-scale gradual integration CNN for false positive reduction in pulmonary nodule detection. / Kim, Bum Chae; Yoon, Jee Seok; Choi, Jun Sik; Suk, Heung-Il.
In: Neural Networks, Vol. 115, 01.07.2019, p. 1-10.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Multi-scale gradual integration CNN for false positive reduction in pulmonary nodule detection
AU - Kim, Bum Chae
AU - Yoon, Jee Seok
AU - Choi, Jun Sik
AU - Suk, Heung-Il
PY - 2019/7/1
Y1 - 2019/7/1
N2 - Lung cancer is a global and dangerous disease, and its early detection is crucial for reducing the risks of mortality. In this regard, it has been of great interest in developing a computer-aided system for pulmonary nodules detection as early as possible on thoracic CT scans. In general, a nodule detection system involves two steps: (i) candidate nodule detection at a high sensitivity, which captures many false positives and (ii) false positive reduction from candidates. However, due to the high variation of nodule morphological characteristics and the possibility of mistaking them for neighboring organs, candidate nodule detection remains a challenge. In this study, we propose a novel Multi-scale Gradual Integration Convolutional Neural Network (MGI-CNN), designed with three main strategies: (1) to use multi-scale inputs with different levels of contextual information, (2) to use abstract information inherent in different input scales with gradual integration, and (3) to learn multi-stream feature integration in an end-to-end manner. To verify the efficacy of the proposed network, we conducted exhaustive experiments on the LUNA16 challenge datasets by comparing the performance of the proposed method with state-of-the-art methods in the literature. On two candidate subsets of the LUNA16 dataset, i.e., V1 and V2, our method achieved an average CPM of 0.908 (V1) and 0.942 (V2), outperforming comparable methods by a large margin. Our MGI-CNN is implemented in Python using TensorFlow and the source code is available from https://github.com/ku-milab/MGICNN.
AB - Lung cancer is a global and dangerous disease, and its early detection is crucial for reducing the risks of mortality. In this regard, it has been of great interest in developing a computer-aided system for pulmonary nodules detection as early as possible on thoracic CT scans. In general, a nodule detection system involves two steps: (i) candidate nodule detection at a high sensitivity, which captures many false positives and (ii) false positive reduction from candidates. However, due to the high variation of nodule morphological characteristics and the possibility of mistaking them for neighboring organs, candidate nodule detection remains a challenge. In this study, we propose a novel Multi-scale Gradual Integration Convolutional Neural Network (MGI-CNN), designed with three main strategies: (1) to use multi-scale inputs with different levels of contextual information, (2) to use abstract information inherent in different input scales with gradual integration, and (3) to learn multi-stream feature integration in an end-to-end manner. To verify the efficacy of the proposed network, we conducted exhaustive experiments on the LUNA16 challenge datasets by comparing the performance of the proposed method with state-of-the-art methods in the literature. On two candidate subsets of the LUNA16 dataset, i.e., V1 and V2, our method achieved an average CPM of 0.908 (V1) and 0.942 (V2), outperforming comparable methods by a large margin. Our MGI-CNN is implemented in Python using TensorFlow and the source code is available from https://github.com/ku-milab/MGICNN.
KW - False positive reduction
KW - Lung cancer screening
KW - Multi-scale convolutional neural network
KW - Multi-stream feature integration
KW - Pulmonary nodule detection
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UR - http://www.scopus.com/inward/citedby.url?scp=85063187357&partnerID=8YFLogxK
U2 - 10.1016/j.neunet.2019.03.003
DO - 10.1016/j.neunet.2019.03.003
M3 - Article
C2 - 30909118
AN - SCOPUS:85063187357
VL - 115
SP - 1
EP - 10
JO - Neural Networks
JF - Neural Networks
SN - 0893-6080
ER -