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Title: Enumerated sparse extraction of important surgical planning features for mandibular reconstruction
Authors: Nagai, Kazuki
Nakao, Megumi  kyouindb  KAKEN_id  orcid (unconfirmed)
Ueda, Nobuhiro
Imai, Yuichiro
Kirita, Tadaaki
Matsuda, Tetsuya  kyouindb  KAKEN_id  orcid (unconfirmed)
Author's alias: 永井, 一希
中尾, 恵
松田, 哲也
Keywords: Feature extraction
Image reconstruction
Biomedical imaging
Linear programming
Issue Date: 2020
Publisher: Institute of Electrical and Electronics Engineers Inc.
Journal title: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Start page: 5519
End page: 5522
Thesis number: 9176601
Abstract: Because implicit medical knowledge and experience are used to perform medical treatment, such decisions must be clarified when systematizing surgical procedures. We propose an algorithm that extracts low-dimensional features that are important for determining the number of fibular segments in mandibular reconstruction using the enumeration of Lasso solutions (eLasso). To perform the multi-class classification, we extend the eLasso using an importance evaluation criterion that quantifies the contribution of the extracted features. Experiment results show that the extracted 7-dimensional feature set has the same estimation performance as the set using all 49-dimensional features.
Description: [2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC 2020); Montreal, Quebec, Canada, 20-24 July 2020]
Rights: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
This is not the published version. Please cite only the published version. この論文は出版社版でありません。引用の際には出版社版をご確認ご利用ください。
DOI(Published Version): 10.1109/EMBC44109.2020.9176601
PubMed ID: 33019229
Appears in Collections:Journal Articles

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