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However, these approaches are limited to analyzing the patches in a fixed shape, while the malignant lesions can form varied shapes. To address this challenge, in this article we propose a Multi-Instance Multi-Shape Support Vector Machine (MIMSSVM) to analyze the multiple images (instances) jointly where each instance consists of multiple patches in various shapes. In our approach, we can identify the different morphologic abnormalities of nuclei shapes from the multiple images. In addition to the multi-instance multi-shape learning capability, we derive an efficient solution algorithm to optimize the proposed model that scales well to a large number of features. Our experimental results show our new method outperforms the existing SVMs and deep learning models in histopathological classification. The proposed model also identifies the tissue segments in an image exhibiting an indication of an abnormality which provides utility in the early detection of malignant tumors. All these promising experimental results have demonstrated the effectiveness of our new method. We anticipate that our new method is of interest to biomedical engineering communities beyond WSI research and have open sourced the code of our method online. The implementation of our proposed MIMSSVM model is publicly available at\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/hoonseo0409\/MIMSSVM\">https:\/\/github.com\/hoonseo0409\/MIMSSVM<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3747593","type":"journal-article","created":{"date-parts":[[2025,8,4]],"date-time":"2025-08-04T15:01:17Z","timestamp":1754319677000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Scalable Multi-Instance Multi-Shape Support Vector Machine for Whole Slide Breast Histopathology"],"prefix":"10.1145","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6766-5926","authenticated-orcid":false,"given":"Hoon","family":"Seo","sequence":"first","affiliation":[{"name":"Computer Science, Colorado School of Mines, Golden, Colorado, USA and X-ray Science Division, Argonne National Laboratory, Lemont, Illinois, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-6295-5871","authenticated-orcid":false,"given":"Yuze","family":"Bai","sequence":"additional","affiliation":[{"name":"University College London, London, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6296-2895","authenticated-orcid":false,"given":"Lodewijk","family":"Brand","sequence":"additional","affiliation":[{"name":"Computer Science, Colorado School of Mines, Golden, Colorado, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3708-9368","authenticated-orcid":false,"given":"Lucia","family":"Saldana Barco","sequence":"additional","affiliation":[{"name":"Computer Science, Colorado School of Mines, Golden, Colorado, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5986-7413","authenticated-orcid":false,"given":"Hua","family":"Wang","sequence":"additional","affiliation":[{"name":"Xi\u2019an Institute of Optics and Precision Mechanics of CAS, Xi\u2019an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,9,18]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"561","volume-title":"Proceedings of the 16th International Conference on Neural Information Processing Systems (NIPS)","author":"Andrews Stuart","year":"2002","unstructured":"Stuart Andrews, Ioannis Tsochantaridis, and Thomas Hofmann. 2002. 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