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Cardiac sarcoidosis classification with deep convolutional neural network-based features using polar maps
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| Title: | Cardiac sarcoidosis classification with deep convolutional neural network-based features using polar maps |
| Authors: | Togo, Ren Browse this author | | Hirata, Kenji Browse this author →KAKEN DB | | Manabe, Osamu Browse this author →KAKEN DB | | Ohira, Hiroshi Browse this author →KAKEN DB | | Tsujino, Ichizo Browse this author →KAKEN DB | | Magota, Keiichi Browse this author →KAKEN DB | | Ogawa, Takahiro Browse this author →KAKEN DB | | Haseyama, Miki Browse this author →KAKEN DB | | Shiga, Tohru Browse this author →KAKEN DB |
| Keywords: | Deep learning | | Convolutional neural network (CNN) | | Cardiac sarcoidosis (CS) | | 18F-FDG PET | | Computer-aided diagnosis | | Radiology | | Machine learning | | Feature extraction | | Feature selection |
| Issue Date: | Jan-2019 |
| Publisher: | Elsevier |
| Journal Title: | Computers in Biology and Medicine |
| Volume: | 104 |
| Start Page: | 81 |
| End Page: | 86 |
| Publisher DOI: | 10.1016/j.compbiomed.2018.11.008 |
| Abstract: | Aims: The aim of this study was to determine whether deep convolutional neural network (DCNN)-based features can represent the difference between cardiac sarcoidosis (CS) and non-CS using polar maps. Methods: A total of 85 patients (33 CS patients and 52 non-CS patients) were analyzed as our study subjects. One radiologist reviewed PET/CT images and defined the left ventricle region for the construction of polar maps. We extracted high-level features from the polar maps through the Inception-v3 network and evaluated their effectiveness by applying them to a CS classification task. Then we introduced the ReliefF algorithm in our method. The standardized uptake value (SUV)-based classification method and the coefficient of variance (CoV)-based classification method were used as comparative methods. Results: Sensitivity, specificity and the harmonic mean of sensitivity and specificity of our method with the ReliefF algorithm were 0.839, 0.870 and 0.854, respectively. Those of the SUVmax-based classification method were 0.468, 0.710 and 0.564, respectively, and those of the CoV-based classification method were 0.655, 0.750 and 0.699, respectively. Conclusion: The DCNN-based high-level features may be more effective than low-level features used in conventional quantitative analysis methods for CS classification. |
| Rights: | © 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ | | https://creativecommons.org/licenses/by-nc-nd/4.0/ |
| Type: | article (author version) |
| URI: | http://hdl.handle.net/2115/76454 |
| Appears in Collections: | 情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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Submitter: 藤後 廉
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