{"id":205380,"created":"2025-01-19T01:07:32.239545+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00205380","sets":["6504:10247:10254"]},"path":["10254"],"owner":"6748","recid":"205380","title":["CNNに対する可視化手法の計算機実験による比較評価"],"pubdate":{"attribute_name":"公開日","attribute_value":"2020-02-20"},"_buckets":{"deposit":"806ae2b8-27bf-4cec-971e-d927390c2267"},"_deposit":{"id":"205380","pid":{"type":"depid","value":"205380","revision_id":0},"owners":[6748],"status":"published","created_by":6748},"item_title":"CNNに対する可視化手法の計算機実験による比較評価","author_link":["509575","509576"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"CNNに対する可視化手法の計算機実験による比較評価"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"人工知能と認知科学","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2020-02-20","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_22_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"明大"},{"subitem_text_value":"明大"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/205380/files/IPSJ-Z82-1U-08.pdf","label":"IPSJ-Z82-1U-08.pdf"},"date":[{"dateType":"Available","dateValue":"2020-06-19"}],"format":"application/pdf","filename":"IPSJ-Z82-1U-08.pdf","filesize":[{"value":"298.0 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"108128a5-0f32-437a-9298-624a93e7a7b2","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2020 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"下村, 真生"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"中村, 和幸"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_22_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00349328","subitem_source_identifier_type":"NCID"}]},"item_22_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"深層学習を用いた医用画像診断支援において、診断根拠の可視化は医師を支援する観点から重要である。発表者が提案した可視化手法の選択指標Black Average Drop (BAD)は、既存のAverage Dropを医用画像へ適用可能な手法へ変更することでGrad-CAM等に比べ小病変を見逃しにくいGrad-CAM++が最適という、知見に合った評価ができる。本発表では、ImageNetのような一般画像への適用やsemantic segmentation分野での評価指標であるmIoUとの比較によるBADの効果検証に加え、モデル精度との関係性、特徴量マップのリサイズ手法比較への応用について発表する。","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"530","bibliographic_titles":[{"bibliographic_title":"第82回全国大会講演論文集"}],"bibliographicPageStart":"529","bibliographicIssueDates":{"bibliographicIssueDate":"2020-02-20","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2020"}]},"relation_version_is_last":true,"weko_creator_id":"6748"},"updated":"2025-01-19T19:56:24.668594+00:00","links":{}}