Multi-order Transfer Learning for Pathologic Diagnosis of Pulmonary Nodule Malignancy

Precise diagnosis of pulmonary nodules can be very crucial as pulmonary nodules are often the common manifestation of early lung cancers. In this study, we investigate the multi-order transfer learning for the assessments of pulmonary nodules to leverage the classification performance of nodules wit...

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Published in:2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) pp. 2813 - 2815
Main Authors: Wang, Qingfeng, Cheng, Jie-Zhi, Liu, Zhiqin, Huang, Jun, Liu, Qiyu, Zhou, Ying, Xu, Weiyun, Wang, Chao, Zhou, Xuehai
Format: Conference Proceeding
Language:English
Published: IEEE 01-12-2018
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Abstract Precise diagnosis of pulmonary nodules can be very crucial as pulmonary nodules are often the common manifestation of early lung cancers. In this study, we investigate the multi-order transfer learning for the assessments of pulmonary nodules to leverage the classification performance of nodules with pathologic confirmation in the condition of small samples. The experiments show that the 3rd, order transfer with the source tasks of texture, diameter and lobulation can achieve the best performance (Acc=0.8194, AUC=0.7533) among all 10 orders transfer learning in the pathologic diagnosis (golden standard) of nodule malignancy, which shows a higher performance than the state-of-the art methods and even outperforms radiologists' performance (Acc=0.7241, AUC=0.76) in terms of Accuracy. This multi-order transfer learning is shown to be effective in the pathologic diagnosis of pulmonary nodule malignancy with simply need only 30% semantic tasks as source tasks.
AbstractList Precise diagnosis of pulmonary nodules can be very crucial as pulmonary nodules are often the common manifestation of early lung cancers. In this study, we investigate the multi-order transfer learning for the assessments of pulmonary nodules to leverage the classification performance of nodules with pathologic confirmation in the condition of small samples. The experiments show that the 3rd, order transfer with the source tasks of texture, diameter and lobulation can achieve the best performance (Acc=0.8194, AUC=0.7533) among all 10 orders transfer learning in the pathologic diagnosis (golden standard) of nodule malignancy, which shows a higher performance than the state-of-the art methods and even outperforms radiologists' performance (Acc=0.7241, AUC=0.76) in terms of Accuracy. This multi-order transfer learning is shown to be effective in the pathologic diagnosis of pulmonary nodule malignancy with simply need only 30% semantic tasks as source tasks.
Author Zhou, Xuehai
Cheng, Jie-Zhi
Liu, Zhiqin
Liu, Qiyu
Wang, Chao
Wang, Qingfeng
Zhou, Ying
Huang, Jun
Xu, Weiyun
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  surname: Wang
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  givenname: Jie-Zhi
  surname: Cheng
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  organization: School of Software Engineering, University of Science and Technology of China, Hefei, China
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  givenname: Xuehai
  surname: Zhou
  fullname: Zhou, Xuehai
  organization: School of Software Engineering, University of Science and Technology of China, Hefei, China
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Snippet Precise diagnosis of pulmonary nodules can be very crucial as pulmonary nodules are often the common manifestation of early lung cancers. In this study, we...
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SubjectTerms Cancer
Computed tomography
computed tomography (CT)
computer-aided diagnosis
Lung
multi-order transfer learning
Neurons
pathologic diagnosis
pulmonary nodule
Semantics
Task analysis
Title Multi-order Transfer Learning for Pathologic Diagnosis of Pulmonary Nodule Malignancy
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