Computational Pathology with Topological signatures and Visual Word Encoding

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.