{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:14:38Z","timestamp":1758672878617,"version":"3.44.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>Histopathological examination primarily relies on hematoxylin and eosin (H&amp;E) and immunohistochemical (IHC) staining. Though IHC provides more crucial molecular information for diagnosis, it is more costly than H&amp;E staining. Stain transfer technology seeks to efficiently generate virtual IHC images from H&amp;E images. While current deep learning-based methods have made progress, they still struggle to maintain pathological and structural consistency across biomarkers without pixel-level aligned reference. To address the problem, we propose an Auxiliary Task supervision-based Stain Transfer method for multi-biomarkers (ATST-Net), which pioneeringly employs human annotation-free masks as ground truth (GT). ATST-Net ensures pathological consistency, structural preservation and style transfer. It automatically annotates H&amp;E masks in a cost-effective manner by utilizing consecutive IHC sections. Multiple auxiliary tasks provide diverse supervisory information on the location and intensity of biomarker expression, ensuring model accuracy and interpretability. We design a pretrained model-based generator to extract deep feature in H&amp;E images, improving generalization performance. Extensive experiments demonstrate the effectiveness of ATST-Net's components. Compared to existing methods, ATST-Net achieves state-of-the-art (SOTA) accuracy on datasets with multiple biomarkers and intensity levels, while also reflecting high practical value. Code is available at https:\/\/github.com\/SikangSHU\/ATST-Net.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/236","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"2116-2124","source":"Crossref","is-referenced-by-count":0,"title":["Advancing Stain Transfer for Multi-Biomarkers: A Human Annotation-Free Method Based on Auxiliary Task Supervision"],"prefix":"10.24963","author":[{"given":"Siyuan","family":"Xu","sequence":"first","affiliation":[{"name":"East China Normal University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haofei","family":"Song","sequence":"additional","affiliation":[{"name":"East China Normal University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingjiao","family":"Deng","sequence":"additional","affiliation":[{"name":"East China Normal University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiansheng","family":"Wang","sequence":"additional","affiliation":[{"name":"East China Normal University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Wang","sequence":"additional","affiliation":[{"name":"East China Normal University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingli","family":"Li","sequence":"additional","affiliation":[{"name":"East China Normal University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2025","name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","start":{"date-parts":[[2025,8,16]]},"theme":"Artificial Intelligence","location":"Montreal, Canada","end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:33:23Z","timestamp":1758627203000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/236"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/236","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}