  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.
