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Computational Pathology with Topological signatures and Visual Word Encoding.

Taymaz Akan1, Richa Aishwarya1, Md Shenuarin Bhuiyan1

  • 1LSU Health Shreveport.

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|December 11, 2025
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Summary
This summary is machine-generated.

This study introduces TopoBoW, a computational framework combining Topological Data Analysis and Bag-of-Visual-Words, to accurately classify muscle tissue. TopoBoW integrates global and local image features for improved pathological analysis.

Keywords:
Computational pathologyMachin LearningPersistent homologyTopological data analysisimage classification

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Area of Science:

  • Computational pathology
  • Digital pathology
  • Medical image analysis

Background:

  • Tissue analysis is crucial for diagnosing disorders but relies on labor-intensive manual evaluation by pathologists.
  • Current computational pathology models struggle to capture both local and global structural patterns and their spatial organization.
  • There is a need for objective computational frameworks to characterize morphological patterns in microscopy images.

Purpose of the Study:

  • To develop TopoBoW, a novel computational framework integrating Topological Data Analysis (TDA) and Bag-of-Visual-Words (BoVW) for objective morphological pattern characterization.
  • To combine global structural features (Betti curves from TDA) with local textural patterns (SURF descriptors from BoVW) for enhanced image analysis.
  • To train an attention-guided multi-layer perceptron (MLP) using TopoBoW features to distinguish between healthy and pathological muscle tissue.

Main Methods:

  • Developed TopoBoW by integrating TDA for global structural features and BoVW for local textural features.
  • Utilized Betti curves from persistent homology (TDA) and SURF descriptors with histogram encoding (BoVW).
  • Employed an attention-guided MLP trained on integrated features and compared performance against baseline models (TDA, HOG with XGBoost, Attention-based MLP).

Main Results:

  • TopoBoW demonstrated state-of-the-art performance in muscle tissue classification.
  • The framework significantly outperformed all baseline models across key classification metrics, including accuracy, F1-score, and AUC.
  • Visualizations confirmed the discriminative ability of TopoBoW's feature vectors across healthy and diseased tissue classes.

Conclusions:

  • TopoBoW provides an interpretable, feature-based computational framework for objective pathological analysis.
  • The integration of global structural and local textural information enhances the characterization of morphological patterns.
  • TopoBoW has the potential to support pathological research, education, and interactive diagnostic workflows.