{"type":"ScholarlyArticle","title":"Advancing Stain Transfer for Multi-Biomarkers: A Human Annotation-Free Method Based on Auxiliary Task Supervision","authors":[{"name":"Siyuan Xu","slug":"siyuan-xu","given":"Siyuan","family":"Xu","affiliation":""},{"name":"Haofei Song","slug":"haofei-song","given":"Haofei","family":"Song","affiliation":""},{"name":"Yingjiao Deng","slug":"yingjiao-deng","given":"Yingjiao","family":"Deng","affiliation":""},{"name":"Jiansheng Wang","slug":"jiansheng-wang","given":"Jiansheng","family":"Wang","affiliation":""},{"name":"Yan Wang","slug":"yan-wang","given":"Yan","family":"Wang","affiliation":""},{"name":"Qingli Li","slug":"qingli-li","given":"Qingli","family":"Li","affiliation":""}],"abstract":"Histopathological examination primarily relies on hematoxylin and eosin (H&E) and immunohistochemical (IHC) staining. Though IHC provides more crucial molecular information for diagnosis, it is more costly than H&E staining. Stain transfer technology seeks to efficiently generate virtual IHC images from H&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&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&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.","doi":"10.24963/ijcai.2025/236","url":"https://ijcai.org/proceedings/2025/236","pdf":"https://ijcai.org/proceedings/2025/0236.pdf","markdown":"https://ijcai.org/proceedings/2025/236.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/236","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Computer Vision","track":"Main Track","number":236,"pages":{"first":2116,"last":2124},"keywords":["Multidisciplinary Topics and Applications: MTA: Bioinformatics","Computer Vision: CV: Biomedical image analysis","Multidisciplinary Topics and Applications: MTA: Health and medicine"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}