AI Insight
TopoMIL is a new computational framework that improves disease diagnosis from microscopic images by incorporating topological information about cell and tissue distribution into multiple instance learning classifiers. The researchers tested three different topological representations on four diagnostic datasets and found that adding topological structure information improved classification performance across multiple pooling methods, with accuracy gains ranging from 0.5% to 5.9% in AUCROC metrics. The framework can be integrated into existing morphology-based models with moderate computational cost.
Why it matters
This approach could enhance automated diagnostic tools in pathology by better capturing spatial patterns in tissue samples that pathologists use for diagnosis. The framework's compatibility with existing models makes it a practical addition to computational pathology workflows, potentially improving accuracy in cancer diagnosis and other diseases detected through microscopic examination.
Understand the Science
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Microscopic images of cells and tissues are central to disease diagnosis. In computational pathology, multiple instance learning (MIL) has emerged as a key paradigm for analyzing numerous images within a single patient sample. While the representative distribution of cells in a sample is important for diagnosis, existing MIL frameworks largely overlook it. We introduce TopoMIL, a framework that extracts the representative topological structure of the sample and integrates it into the MIL classifier. Three topological representations are assessed, each with distinct advantages and computational costs. We evaluate TopoMIL on four histopathology and cytomorphology datasets, each presenting unique challenges. Integrating the sample’s topological information into MIL enhances classification across average, max, attention-based, and transformer pooling, yielding AUCROC gains of 3.3%, 4.2%, 5.9%, and 0.5%, respectively, with moderate computational cost. Our work underscores the potential of TopoMIL as a scalable extension to existing morphology-based models in computational pathology.
Source: TopoMIL: Topology Improves Multiple Instance Learning in Diagnostic Microscopic Images