@inproceedings{oai:kyutech.repo.nii.ac.jp:00004647,
 author = {Li, Guangxu and Kim, Hyungseop and 金, 亨燮 and Tan, Joo kooi and タン, ジュークイ and Ishikawa, Seiji and 石川, 聖二 and Hirano, Yasushi and Kido, Shoji and Tachibana, Rie},
 book = {2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
 month = {Sep},
 note = {Since it is difficult to choose which computer calculated features are effective to predict the malignancy of pulmonary nodules, in this study, we add a semantic-level of Artificial Neural Networks (ANNs) structure to improve intuition of features selection. The works of this study include two: 1) seeking the relationships between computer-calculated features and medical semantic concepts which could be understood by human; 2) providing an objective assessment method to predict the malignancy from semantic characteristics. We used 60 thoracic CT scans collected from the Lung Image Database Consortium (LIDC) database, in which the suspicious lesions had been delineated and annotated by 4 radiologists independently. Corresponding to the two works of this study, correlation analysis experiment and agreement experiment were performed separately., The 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'13), July 3-7, 2013, Osaka, Japan},
 pages = {5465--5468},
 publisher = {IEEE},
 title = {Semantic Characteristics Prediction of Pulmonary Nodule Using Artificial Neural Networks},
 year = {2013},
 yomi = {イシカワ, セイジ}
}