[2307.14025] Topological Inductive Bias fosters Multiple Instance Learning in Data-Scarce Scenarios

Computer Science > Machine Learning arXiv:2307.14025 (cs) [Submitted on 26 Jul 2023 (v1), last revised 2 Mar 2026 (this version, v3)] Title:Topological Inductive Bias fosters Multiple Instance Learning in Data-Scarce Scenarios Authors:Salome Kazeminia, Carsten Marr, Bastian Rieck View a PDF of the paper titled Topological Inductive Bias fosters Multiple Instance Learning in Data-Scarce Scenarios, by Salome Kazeminia and 2 other authors View PDF HTML (experimental) Abstract:Multiple instance learning (MIL) is a framework for weakly supervised classification, where labels are assigned to sets of instances, i.e., bags, rather than to individual data points. This paradigm has proven effective in tasks where fine-grained annotations are unavailable or costly to obtain. However, the effectiveness of MIL drops sharply when training data are scarce, such as for rare disease classification. To address this challenge, we propose incorporating topological inductive biases into the data representation space within the MIL framework. This bias introduces a topology-preserving constraint that encourages the instance encoder to maintain the topological structure of the instance distribution within each bag when mapping them to MIL latent space. As a result, our Topology Guided MIL (TG-MIL) method enhances the performance and generalizability of MIL classifiers across different aggregation functions, especially under scarce-data regimes. Our evaluations show average performance improvemen...