Topologically Regularized Multiple Instance Learning to Harness Data Scarcity

arXiv:2307.14025v2 Announce Type: replace
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 …

abstract analysis arxiv availability biomedical challenge cs.cv cs.lg data data analysis diseases eess.iv harness however instance microscopy mil multiple patients q-bio.qm rare diseases regularization samples stat.ml tool type