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Updated: May 5, 2026

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Mind the Gap: Evaluating Patch Embeddings from General-Purpose and Histopathology Foundation Models for Cell

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    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
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    Summary

    Histopathology foundation models outperform general models for cell analysis tasks like segmentation and classification. This study quantifies the representation learning gap, guiding future model selection in digital pathology and neuroscience.

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    Area of Science:

    • Digital pathology
    • Computer vision
    • Computational neuroscience

    Background:

    • Foundation models have advanced computer vision, impacting digital histopathology.
    • The efficacy of domain-specific histopathology foundation models versus general models for cell analysis is not well understood.

    Purpose of the Study:

    • Investigate the representation learning differences between general-purpose and histopathology-specific foundation models.
    • Analyze multi-level patch embeddings for cell instance segmentation and classification.

    Main Methods:

    • Utilized an encoder-decoder architecture with frozen encoders (general-purpose and histopathology-specific).
    • Integrated multi-level patch embeddings via skip connections.
    • Generated semantic and distance maps for instance segmentation and cell-type classification.
    • Evaluated performance on PanNuke, CoNIC, and CytoDArk0 datasets.

    Main Results:

    • Histopathology foundation models demonstrated superior performance in cell instance segmentation and classification.
    • Comparative analysis revealed significant differences in feature representation learning.
    • Performance varied across different encoder architectures and pre-training datasets.

    Conclusions:

    • Domain-specific histopathology foundation models offer advantages over general models for cell-level analysis.
    • Findings provide crucial guidance for selecting appropriate foundation models in digital pathology and brain cytoarchitecture studies.
    • Highlights the importance of specialized pre-training for histopathology tasks.