{"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/topologically-regularized-multiple-instance","title":"Topologically Regularized Multiple Instance Learning to Harness Data Scarcity","arxiv_id":"2307.14025","date":"2023-07-26","proceeding":null,"authors":["Salome Kazeminia","Carsten Marr","Bastian Rieck"],"abstract":"In biomedical data analysis, Multiple Instance Learning (MIL) models have emerged as a powerful tool to classify patients' microscopy samples. However, the data-intensive requirement of these models poses a significant challenge in scenarios with scarce data availability, e.g., in rare diseases. We introduce a topological regularization term to MIL to mitigate this challenge. It provides a shape-preserving inductive bias that compels the encoder to maintain the essential geometrical-topological structure of input bags during projection into latent space. This enhances the performance and generalization of the MIL classifier regardless of the aggregation function, particularly for scarce training data. The effectiveness of our method is confirmed through experiments across a range of datasets, showing an average enhancement of 2.8% for MIL benchmarks, 15.3% for synthetic MIL datasets, and 5.5% for real-world biomedical datasets over the current state-of-the-art.","url_abs":"https://arxiv.org/abs/2307.14025v2","url_pdf":"https://arxiv.org/pdf/2307.14025v2.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":"topologically-regularized-multiple-instance","repo_url":"https://github.com/lmbda-design/rgmil","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"inductive-bias","task_name":"Inductive Bias"},{"task_slug":"multiple-instance-learning","task_name":"Multiple Instance Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}