{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/fluoroformer-scaling-multiple-instance","title":"Fluoroformer: Scaling multiple instance learning to multiplexed images via attention-based channel fusion","arxiv_id":"2411.08975","date":"2024-11-13","proceeding":null,"authors":["Marc Harary","Eliezer M. Van Allen","William Lotter"],"abstract":"Though multiple instance learning (MIL) has been a foundational strategy in computational pathology for processing whole slide images (WSIs), current approaches are designed for traditional hematoxylin and eosin (H&E) slides rather than emerging multiplexed technologies. Here, we present an MIL strategy, the Fluoroformer module, that is specifically tailored to multiplexed WSIs by leveraging scaled dot-product attention (SDPA) to interpretably fuse information across disparate channels. On a cohort of 434 non-small cell lung cancer (NSCLC) samples, we show that the Fluoroformer both obtains strong prognostic performance and recapitulates immuno-oncological hallmarks of NSCLC. Our technique thereby provides a path for adapting state-of-the-art AI techniques to emerging spatial biology assays.","url_abs":"https://arxiv.org/abs/2411.08975v1","url_pdf":"https://arxiv.org/pdf/2411.08975v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"fluoroformer-scaling-multiple-instance","repo_url":"https://github.com/lotterlab/fluoroformer","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"multiple-instance-learning","task_name":"Multiple Instance Learning"},{"task_slug":"whole-slide-images","task_name":"whole slide images"}],"methods":[{"method_slug":"attention","method_name":"Attention"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}