{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00242227","sets":["1164:4619:11919:11920"]},"path":["11920"],"owner":"44499","recid":"242227","title":["Multi-scan Partitioning Visual Mamba for Breast Cancer Segmentation"],"pubdate":{"attribute_name":"公開日","attribute_value":"2025-01-14"},"_buckets":{"deposit":"22362df3-4f82-462e-804d-d26bd8711903"},"_deposit":{"id":"242227","pid":{"type":"depid","value":"242227","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Multi-scan Partitioning Visual Mamba for Breast Cancer Segmentation","author_link":["668517","668516","668514","668515"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Multi-scan Partitioning Visual Mamba for Breast Cancer Segmentation"},{"subitem_title":"Multi-scan Partitioning Visual Mamba for Breast Cancer Segmentation","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":"Waseda University"},{"subitem_text_value":"Waseda University"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Waseda University","subitem_text_language":"en"},{"subitem_text_value":"Waseda University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"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/242227/files/IPSJ-CVIM25240001.pdf","label":"IPSJ-CVIM25240001.pdf"},"date":[{"dateType":"Available","dateValue":"2027-01-14"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-CVIM25240001.pdf","filesize":[{"value":"1.5 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"20"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"376bde3f-6d9e-48c5-8bc9-859fe2a26c8b","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2025 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Fengze, Li"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Seiichiro, Kamata"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Fengze, Li","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Seiichiro, Kamata","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":"Breast cancer is the most commonly diagnosed cancer in women, making it a leading cause of cancer-related deaths worldwide. Accurate diagnosis often relies on Whole-Slide Images (WSIs), a key pathological imaging modality that provides detailed visual information for identifying tumor regions. To facilitate this, advanced image segmentation methods have been developed, with convolutional neural networks (CNNs) and Transformers being the most prevalent approaches. However, CNNs often fail to capture long-range dependencies effectively, while Transformer-based methods suffer from high computational complexity. Recently, Mamba, an emerging method based on the state space model, has gained attention for its ability to address these challenges by capturing long-range dependencies with linear computational complexity. Building on this innovation, we propose the Multi-Scan Partitioning Mamba UNet for WSI segmentation. This approach integrates a novel module, the Multi-Scan Partition State Space (MPSS) module, which we introduce in detail. Extensive experiments validate the efficacy of the proposed method in processing WSIs, demonstrating its superiority over existing benchmarks.","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Breast cancer is the most commonly diagnosed cancer in women, making it a leading cause of cancer-related deaths worldwide. Accurate diagnosis often relies on Whole-Slide Images (WSIs), a key pathological imaging modality that provides detailed visual information for identifying tumor regions. To facilitate this, advanced image segmentation methods have been developed, with convolutional neural networks (CNNs) and Transformers being the most prevalent approaches. However, CNNs often fail to capture long-range dependencies effectively, while Transformer-based methods suffer from high computational complexity. Recently, Mamba, an emerging method based on the state space model, has gained attention for its ability to address these challenges by capturing long-range dependencies with linear computational complexity. Building on this innovation, we propose the Multi-Scan Partitioning Mamba UNet for WSI segmentation. This approach integrates a novel module, the Multi-Scan Partition State Space (MPSS) module, which we introduce in detail. Extensive experiments validate the efficacy of the proposed method in processing WSIs, demonstrating its superiority over existing benchmarks.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"7","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":"1","bibliographicVolumeNumber":"2025-CVIM-240"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":242227,"updated":"2025-01-19T07:23:33.587731+00:00","links":{},"created":"2025-01-19T01:47:18.559931+00:00"}