{"links":{},"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00242239","sets":["1164:4619:11919:11920"]},"path":["11920"],"owner":"44499","recid":"242239","title":["Simplex Noiseを用いた拡散モデルとProgressive Mask Refinementによる医用画像中の異常領域の検出"],"pubdate":{"attribute_name":"公開日","attribute_value":"2025-01-14"},"_buckets":{"deposit":"7d9539e7-7321-4562-97bc-ca381cbb0677"},"_deposit":{"id":"242239","pid":{"type":"depid","value":"242239","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Simplex Noiseを用いた拡散モデルとProgressive Mask Refinementによる医用画像中の異常領域の検出","author_link":["668594","668596","668595","668590","668593","668589","668591","668592"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Simplex Noiseを用いた拡散モデルとProgressive Mask Refinementによる医用画像中の異常領域の検出"},{"subitem_title":"Anomaly Region Detection in Medical Images using Diffusion Models with Simplex Noise and Progressive Mask Refinement","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2025-01-14","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"名古屋工業大学"},{"subitem_text_value":"慶應義塾大学"},{"subitem_text_value":"順天堂大学"},{"subitem_text_value":"名古屋工業大学"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Nagoya Institute of Technology","subitem_text_language":"en"},{"subitem_text_value":"Keio University","subitem_text_language":"en"},{"subitem_text_value":"Juntendo University","subitem_text_language":"en"},{"subitem_text_value":"Nagoya Institute of Technology","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"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/242239/files/IPSJ-CVIM25240013.pdf","label":"IPSJ-CVIM25240013.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-CVIM25240013.pdf","filesize":[{"value":"2.7 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"20"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"f0f5527e-43f9-4570-8d62-f418e7a268b9","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG."}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"飛世, 裕貴"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"橋本, 正弘"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"明石, 敏昭"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"本谷, 秀堅"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Hiroki, Tobise","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Masahiro, Hashimoto","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Toshiaki, Akashi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hidekata, Hontani","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11131797","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8701","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"本稿では自己符号化器として拡散モデルを用いる異常検知法を提案する．拡散モデルによる自己符号化器は，入力画像にノイズを加えることで符号化し，逆拡散することにより復号する．逆拡散過程を正常画像のみに基づく学習により構築することで，入力画像中の異常部位が復号画像に含まれないことを期待する．しかし，ガウシアンノイズを利用する典型的な拡散モデルを用いると異常検知精度が高くなりにくい．本稿では，ガウシアンノイズではなく多種類のSimplex Noiseを符号化に利用し，Progressive Mask Refinementを適用することにより異常検知精度を改善できることを示す．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"In this paper, we propose an anomaly detection method that uses a diffusion model as an autoencoder. The diffusion model-based autoencoder encodes an input image by adding noise, and then decodes it by denoising. By constructing the denoising process by learning only on normal images, we expect that anomaly regions in an input image will not be included in the decoded image. However, when using a typical diffusion model that uses Gaussian noise, it is difficult to improve the accuracy of anomaly detection. In this paper, we show that the accuracy of anomaly detection can be improved by using various types of Simplex Noise instead of Gaussian noise for encoding and applying Progressive Mask Refinement.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告コンピュータビジョンとイメージメディア（CVIM）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2025-01-14","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"13","bibliographicVolumeNumber":"2025-CVIM-240"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:47:19.708629+00:00","updated":"2025-01-19T07:23:21.115828+00:00","id":242239}