PMID- 32285220
OWN - NLM
STAT- MEDLINE
DCOM- 20210816
LR  - 20240329
IS  - 1618-727X (Electronic)
IS  - 0897-1889 (Print)
IS  - 0897-1889 (Linking)
VI  - 33
IP  - 4
DP  - 2020 Aug
TI  - Combining multi-scale feature fusion with multi-attribute grading, a CNN model 
      for benign and malignant classification of pulmonary nodules.
PG  - 869-878
LID - 10.1007/s10278-020-00333-1 [doi]
AB  - Lung cancer has the highest mortality rate of all cancers, and early detection 
      can improve survival rates. In the recent years, low-dose CT has been widely used 
      to detect lung cancer. However, the diagnosis is limited by the subjective 
      experience of doctors. Therefore, the main purpose of this study is to use 
      convolutional neural network to realize the benign and malignant classification 
      of pulmonary nodules in CT images. We collected 1004 cases of pulmonary nodules 
      from LIDC-IDRI dataset, among which 554 cases were benign and 450 cases were 
      malignant. According to the doctors' annotates on the center coordinates of the 
      nodules, two 3D CT image patches of pulmonary nodules with different scales were 
      extracted. In this study, our work focuses on two aspects. Firstly, we 
      constructed a multi-stream multi-task network (MSMT), which combined multi-scale 
      feature with multi-attribute classification for the first time, and applied it to 
      the classification of benign and malignant pulmonary nodules. Secondly, we 
      proposed a new loss function to balance the relationship between different 
      attributes. The final experimental results showed that our model was effective 
      compared with the same type of study. The area under ROC curve, accuracy, 
      sensitivity, and specificity were 0.979, 93.92%, 92.60%, and 96.25%, 
      respectively.
FAU - Zhao, Jumin
AU  - Zhao J
AD  - College of Information and Computer, Taiyuan University of Technology, Jinzhong, 
      China.
AD  - Technology Research Center of Spatial Information Network Engineering of Shanxi, 
      Jinzhong, China.
FAU - Zhang, Chen
AU  - Zhang C
AD  - College of Information and Computer, Taiyuan University of Technology, Jinzhong, 
      China.
FAU - Li, Dengao
AU  - Li D
AD  - Technology Research Center of Spatial Information Network Engineering of Shanxi, 
      Jinzhong, China. lidengao@tyut.edu.cn.
AD  - College of Data Science, Taiyuan University of Technology, Jinzhong, China. 
      lidengao@tyut.edu.cn.
FAU - Niu, Jing
AU  - Niu J
AD  - College of Information and Computer, Taiyuan University of Technology, Jinzhong, 
      China.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PL  - United States
TA  - J Digit Imaging
JT  - Journal of digital imaging
JID - 9100529
SB  - IM
MH  - Humans
MH  - Lung Neoplasms/diagnostic imaging
MH  - *Multiple Pulmonary Nodules/diagnostic imaging
MH  - Neural Networks, Computer
MH  - Radiographic Image Interpretation, Computer-Assisted
MH  - *Solitary Pulmonary Nodule/diagnostic imaging
MH  - Tomography, X-Ray Computed
PMC - PMC7522130
OTO - NOTNLM
OT  - Convolutional neural network
OT  - Multi-scale feature fusion
OT  - Multi-task learning
OT  - Pulmonary nodule classification
COIS- The authors declare that they have no conflict of interest.
EDAT- 2020/04/15 06:00
MHDA- 2021/08/17 06:00
PMCR- 2021/08/01
CRDT- 2020/04/15 06:00
PHST- 2020/04/15 06:00 [pubmed]
PHST- 2021/08/17 06:00 [medline]
PHST- 2020/04/15 06:00 [entrez]
PHST- 2021/08/01 00:00 [pmc-release]
AID - 10.1007/s10278-020-00333-1 [pii]
AID - 333 [pii]
AID - 10.1007/s10278-020-00333-1 [doi]
PST - ppublish
SO  - J Digit Imaging. 2020 Aug;33(4):869-878. doi: 10.1007/s10278-020-00333-1.