{"created":"2024-11-22T06:30:17.115967+00:00","id":2008080,"links":{},"metadata":{"_buckets":{"deposit":"732af21f-6cd1-4024-8f5c-1d051bd13b33"},"_deposit":{"created_by":7,"id":"2008080","owners":[7],"pid":{"revision_id":0,"type":"depid","value":"2008080"},"status":"published"},"_oai":{"id":"oai:tokushima-u.repo.nii.ac.jp:02008080","sets":["1713853213384:1713853295607"]},"author_link":["401"],"control_number":"2008080","item_10001_alternative_title_1":{"attribute_name":"タイトル別表記","attribute_value_mlt":[{"subitem_alternative_title":"New Classification and Generative Model for Medical Visual Question Answering","subitem_alternative_title_language":"en"}]},"item_10001_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2020-03-11","bibliographicIssueDateType":"Issued"},"bibliographicPageEnd":"50636","bibliographicPageStart":"50626","bibliographicVolumeNumber":"8","bibliographic_titles":[{"bibliographic_title":"IEEE Access","bibliographic_titleLang":"en"}]}]},"item_10001_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"Medical images are playing an important role in the medical domain. A mature medical visual question answering system can aid diagnosis, but there is no satisfactory method to solve this comprehensive problem so far. Considering that there are many different types of questions, we propose a model called CGMVQA, including classification and answer generation capabilities to turn this complex problem into multiple simple problems in this paper. We adopt data augmentation on images and tokenization on texts. We use pre-trained ResNet152 to extract image features and add three kinds of embeddings together to deal with texts. We reduce the parameters of the multi-head self-attention transformer to cut the computational cost down. We adjust the masking and output layers to change the functions of the model. This model establishes new state-of-the-art results: 0.640 of classification accuracy, 0.659 of word matching and 0.678 of semantic similarity in ImageCLEF 2019 VQA-Med data set. It suggests that the CGMVQA is effective in medical visual question answering and can better assist doctors in clinical analysis and diagnosis.","subitem_description_language":"en","subitem_description_type":"Abstract"}]},"item_10001_publisher_8":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"IEEE","subitem_publisher_language":"en"}]},"item_10001_rights_15":{"attribute_name":"権利情報","attribute_value_mlt":[{"subitem_rights":"This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/","subitem_rights_language":"en"}]},"item_10001_source_id_9":{"attribute_name":"収録物ID","attribute_value_mlt":[{"subitem_source_identifier":"21693536","subitem_source_identifier_type":"ISSN"}]},"item_10001_version_type_20":{"attribute_name":"出版タイプ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_970fb48d4fbd8a85","subitem_version_type":"VoR"}]},"item_1715043197608":{"attribute_name":"アクセス権","attribute_value_mlt":[{"subitem_access_right":"open access"}]},"item_1722929371688":{"attribute_name":"出版社版DOI","attribute_value_mlt":[{"subitem_relation_name":[{"subitem_relation_name_language":"ja","subitem_relation_name_text":"10.1109/ACCESS.2020.2980024"}],"subitem_relation_type_id":{"subitem_relation_type_id_text":"https://doi.org/10.1109/ACCESS.2020.2980024","subitem_relation_type_select":"DOI"}}]},"item_1723180141928":{"attribute_name":"EID","attribute_value_mlt":[{"subitem_identifier_type":"URI","subitem_identifier_uri":"364179"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorAffiliations":[{"affiliationNameIdentifiers":[{"affiliationNameIdentifier":"","affiliationNameIdentifierScheme":"ISNI","affiliationNameIdentifierURI":"http://www.isni.org/isni/"}],"affiliationNames":[{"affiliationName":"","affiliationNameLang":"ja"}]}],"creatorNames":[{"creatorName":"任, 福継","creatorNameLang":"ja"},{"creatorName":"ニン, フジ","creatorNameLang":"ja-Kana"},{"creatorName":"Ren, Fuji","creatorNameLang":"en"}],"familyNames":[{"familyName":"任","familyNameLang":"ja"},{"familyName":"ニン","familyNameLang":"ja-Kana"},{"familyName":"Ren","familyNameLang":"en"}],"givenNames":[{"givenName":"福継","givenNameLang":"ja"},{"givenName":"フジ","givenNameLang":"ja-Kana"},{"givenName":"Fuji","givenNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"401","nameIdentifierScheme":"WEKO"},{"nameIdentifier":"19966/profile-ja.html","nameIdentifierScheme":"徳島大学 教育研究者総覧","nameIdentifierURI":"http://pub2.db.tokushima-u.ac.jp/ERD/person/19966/profile-ja.html"},{"nameIdentifier":"20264947","nameIdentifierScheme":"e-Rad","nameIdentifierURI":"https://nrid.nii.ac.jp/ja/search/?qm=20264947"}]},{"creatorNames":[{"creatorName":"Zhou, Yangyang","creatorNameLang":"en"}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_access","date":[{"dateType":"Available","dateValue":"2021-02-08"}],"displaytype":"detail","filename":"access_8_50626.pdf","filesize":[{"value":"2.16 MB"}],"format":"application/pdf","licensetype":"license_0","mimetype":"application/pdf","url":{"objectType":"fulltext","url":"https://tokushima-u.repo.nii.ac.jp/record/2008080/files/access_8_50626.pdf"},"version_id":"0c4d6320-7208-451a-b548-f0f061674309"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"Classification model","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"generative model","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"medical image","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"transformer","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"visual question answering","subitem_subject_language":"en","subitem_subject_scheme":"Other"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"journal article","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"CGMVQA : A New Classification and Generative Model for Medical Visual Question Answering","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"CGMVQA : A New Classification and Generative Model for Medical Visual Question Answering","subitem_title_language":"en"}]},"item_type_id":"40001","owner":"7","path":["1713853295607"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2021-02-08"},"publish_date":"2021-02-08","publish_status":"0","recid":"2008080","relation_version_is_last":true,"title":["CGMVQA : A New Classification and Generative Model for Medical Visual Question Answering"],"weko_creator_id":"7","weko_shared_id":-1},"updated":"2025-02-13T05:58:30.502802+00:00"}