{"type":"ScholarlyArticle","title":"GroupMIL: Semantic Group Based Multiple Instance Learning for Whole Slide Image Analysing","authors":[{"name":"Zhao Yao","slug":"zhao-yao","given":"Zhao","family":"Yao","affiliation":""},{"name":"Zhenmi Xie","slug":"zhenmi-xie","given":"Zhenmi","family":"Xie","affiliation":""},{"name":"Mengxin Tian","slug":"mengxin-tian","given":"Mengxin","family":"Tian","affiliation":""},{"name":"Guoqing Wu","slug":"guoqing-wu","given":"Guoqing","family":"Wu","affiliation":""},{"name":"Yaonan Wang","slug":"yaonan-wang","given":"Yaonan","family":"Wang","affiliation":""},{"name":"Min Liu","slug":"min-liu","given":"Min","family":"Liu","affiliation":""}],"abstract":"Whole Slide Image (WSI) analysis faces challenges due to gigapixel resolutions and slide-level weak supervision. Multiple Instance Learning (MIL) serves as a pivotal method for this task. However, existing MIL frameworks often fail to exploit the inherent redundancy of tissue patterns or the semantic coherence among similar patches within a WSI. We propose GroupMIL, a novel framework that introduces a differentiable grouping mechanism into the MIL framework. This approach enables the automatic emergence of semantic segments using only slide-level labels. We specifically introduce a multi-stage grouping block and a hierarchical aggregator, which progressively fuse features within and across groups to construct a robust slide-level representation. Extensive experiments on multiple public datasets across cancer subtyping and survival prediction tasks demonstrate that GroupMIL consistently surpasses state-of-the-art performance.","doi":"10.24963/ijcai.2026/778","url":"https://ijcai.org/proceedings/2026/778","pdf":"https://ijcai.org/proceedings/2026/0778.pdf","markdown":"https://ijcai.org/proceedings/2026/778.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/778","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"AI and Health","track":"AI and Health","number":778,"pages":{"first":6993,"last":7000},"keywords":["Medical diagnosis: Medical diagnosis","Health data mining: Health data mining","Medical imaging: Medical imaging"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}