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 |
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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. |
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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 |
Author_xml | – sequence: 1 givenname: Qingfeng surname: Wang fullname: Wang, Qingfeng organization: School of Software Engineering, University of Science and Technology of China, Hefei, China – sequence: 2 givenname: Jie-Zhi surname: Cheng fullname: Cheng, Jie-Zhi organization: Shanghai United Imaging Intelligence Co. Ltd., Shanghai, China – sequence: 3 givenname: Zhiqin surname: Liu fullname: Liu, Zhiqin organization: School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, China – sequence: 4 givenname: Jun surname: Huang fullname: Huang, Jun organization: School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, China – sequence: 5 givenname: Qiyu surname: Liu fullname: Liu, Qiyu organization: Radiology Department, Mianyang Central Hospital, Mianyang, China – sequence: 6 givenname: Ying surname: Zhou fullname: Zhou, Ying organization: Radiology Department, Mianyang Central Hospital, Mianyang, China – sequence: 7 givenname: Weiyun surname: Xu fullname: Xu, Weiyun organization: Breast Surgery Mianyang Central Hospital, Mianyang, China – sequence: 8 givenname: Chao surname: Wang fullname: Wang, Chao organization: School of Software Engineering, University of Science and Technology of China, Hefei, China – sequence: 9 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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