{"created":"2023-06-20T16:49:19.349145+00:00","id":21519,"links":{},"metadata":{"_buckets":{"deposit":"b528a71d-16da-4b00-b920-78e6a20980e8"},"_deposit":{"created_by":3,"id":"21519","owners":[3],"pid":{"revision_id":0,"type":"depid","value":"21519"},"status":"published"},"_oai":{"id":"oai:kindai.repo.nii.ac.jp:00021519","sets":["14:2667:4613"]},"author_link":["43191"],"control_number":"21519","item_8_biblio_info_21":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2020","bibliographicIssueDateType":"Issued"},"bibliographicPageEnd":"5","bibliographicPageStart":"1","bibliographic_titles":[{"bibliographic_title":"科学研究費助成事業研究成果報告書 (2019)"}]}]},"item_8_description_25":{"attribute_name":"リンクURL","attribute_value_mlt":[{"subitem_description":"https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-17K17680/","subitem_description_type":"Other"}]},"item_8_description_33":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"研究成果の概要（和文）：深層畳み込みニューラルネットワークなど，AI画像認識処理の効率的な学習方法の１つに，深層転移学習がある。これは，認識対象とは別の画像データを使って画像認識の事前学習を行ったうえで，対象の画像パターン認識処理を構築する方法である。本研究では，医用画像上の病変パターン認識の事前学習データとして，人体内の特徴的な局所パターン（解剖学的ランドマーク：LM）を用いたときの振る舞いについて，実験的に検討した。臨床画像を用いた実験の結果，LMデータを用いて事前学習するだけで，病変認識に有用な複数の特徴量が自動的に生成されることが明らかとなった。研究成果の概要（英文）：The deep transfer learning is efficient deep learning to construct AI image recognition processes, such as a deep convolutional neural network. In the learning, a pre-training is performed with a different image dataset from a target object at first. The target pattern recognizer is constructed based on the pre-trained network. In this study, we evaluated the pre-training by medical image patches, including anatomical landmarks (LMs) for lesion pattern classification. The LM is a unique anatomical structure in the human body. Collecting the LM dataset is more accessible than the lesion data collection because those LMs exist not only in healthy cases but also in malignant cases. Our experiments with the clinical dataset showed that the pre-training with the LM dataset brought effective image features for the lesion classification.","subitem_description_type":"Abstract"}]},"item_8_description_36":{"attribute_name":"内容記述","attribute_value_mlt":[{"subitem_description":"研究種目：若手研究(B);  研究期間：2017～2019;   課題番号：17K17680;   研究分野：医用画像処理;   科研費の分科・細目：","subitem_description_type":"Other"}]},"item_8_description_37":{"attribute_name":"資源タイプ(WEKO2)","attribute_value_mlt":[{"subitem_description":"Research Paper","subitem_description_type":"Other"}]},"item_8_description_41":{"attribute_name":"フォーマット","attribute_value_mlt":[{"subitem_description":"application/pdf","subitem_description_type":"Other"}]},"item_8_publisher_14":{"attribute_name":"出版者 名前","attribute_value_mlt":[{"subitem_publisher":"近畿大学"}]},"item_8_text_10":{"attribute_name":"著者 役割","attribute_value_mlt":[{"subitem_text_value":"研究代表者"}]},"item_8_text_7":{"attribute_name":"著者(英)","attribute_value_mlt":[{"subitem_text_language":"en","subitem_text_value":"Nemoto, Mitsutaka"}]},"item_8_text_8":{"attribute_name":"著者 所属","attribute_value_mlt":[{"subitem_text_value":"近畿大学生物理工学部; 講師"}]},"item_8_text_9":{"attribute_name":"著者所属(翻訳)","attribute_value_mlt":[{"subitem_text_value":"Kindai University"}]},"item_8_version_type_12":{"attribute_name":"版","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_be7fb7dd8ff6fe43","subitem_version_type":"NA"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"根本, 充貴"},{"creatorName":"ネモト, ミツタカ","creatorNameLang":"ja-Kana"}],"nameIdentifiers":[{},{}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2021-03-15"}],"displaytype":"detail","filename":"17K17680seika.pdf","filesize":[{"value":"280.9 kB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"17K17680seika.pdf","url":"https://kindai.repo.nii.ac.jp/record/21519/files/17K17680seika.pdf"},"version_id":"c861bef2-a3b7-4f82-9e69-7e01e26bcca2"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"転移学習","subitem_subject_scheme":"Other"},{"subitem_subject":"深層学習","subitem_subject_scheme":"Other"},{"subitem_subject":"コンピュータ検出支援","subitem_subject_scheme":"Other"},{"subitem_subject":"医用画像診断","subitem_subject_scheme":"Other"},{"subitem_subject":"解剖学的ランドマーク","subitem_subject_scheme":"Other"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"research report","resourceuri":"http://purl.org/coar/resource_type/c_18ws"}]},"item_title":"解剖学的ランドマーク情報を用いた3次元医用画像上の病変認識処理の効率的深層学習","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"解剖学的ランドマーク情報を用いた3次元医用画像上の病変認識処理の効率的深層学習","subitem_title_language":"ja"},{"subitem_title":"An efficient deep learning method to detect lesions on 3D medical images via information of anatomical landmarks","subitem_title_language":"en"}]},"item_type_id":"8","owner":"3","path":["4613"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2021-03-15"},"publish_date":"2021-03-15","publish_status":"0","recid":"21519","relation_version_is_last":true,"title":["解剖学的ランドマーク情報を用いた3次元医用画像上の病変認識処理の効率的深層学習"],"weko_creator_id":"3","weko_shared_id":-1},"updated":"2023-09-14T07:20:48.660300+00:00"}