Multiscale tumor characterization in histopathology via self-distilled transformers and topology-aware visual...

Area of Science:

• Computational pathology
• Digital histopathology
• Machine learning in medicine

Background:

• Whole slide images (WSIs) in histopathology present challenges due to complexity and variations across magnifications.
• Existing models often struggle with generalization, ignore tissue architecture, or lack adaptive sample prioritization.
• Limited integration of pixel-level appearance and morphological context restricts diagnostic reliability.

Purpose of the Study:

• To develop a framework for multiscale tumor characterization that addresses limitations of existing histopathology models.
• To improve accuracy, robustness, and diagnostic reliability across different magnifications and tissue variations.
• To integrate topological, contextual, and morphological information for enhanced tumor analysis.

Main Methods:

• Developed the Pathology-Adaptive Uncertainty-Aware Consistency (PAUAC) Framework for cross-magnification prediction consistency.
• Implemented Structural Attention-Constrained Graph Regularizer (SACGR) for topology-aware visual encoding.
• Introduced Multiscale Pathology Curriculum Scheduler (MPCS) for adaptive sample prioritization during training.
• Integrated Transformer-Driven Dual-Modality Morphometry Network and Contrastive Cell-Contextual Representation Alignment (CCCRA) module.

Main Results:

• Achieved measurable improvements including +2.3% Dice score and +3.7% accuracy for ambiguous samples.
• +21% faster convergence and +12.6% normalized mutual information in embeddings demonstrated enhanced learning efficiency.
• The framework shows enhanced representational richness and embedding consistency across magnifications.

Conclusions:

• The proposed framework offers a significant advancement in multiscale tumor characterization for histopathology.
• It establishes resolution-aware, topology-constrained, and morphology-fused learning in an interpretable and scalable manner.
• This work enhances diagnostic reliability and efficiency in digital pathology analysis.