Multi-cancer framework with cancer-aware attention and adversarial mutual-information minimization for whole slide...

Area of Science:

• Digital Pathology
• Computational Pathology
• Artificial Intelligence in Medicine

Background:

• Whole Slide Images (WSIs) are vital for pathology, providing high-resolution data for diagnosis and research.
• Current deep learning models for WSIs often lack generalizability due to tumor heterogeneity.
• Scalability issues arise from the narrow focus on specific cancer types.

Purpose of the Study:

• To develop a novel, scalable deep learning framework for multi-cancer Whole Slide Image (WSI) analysis.
• To address the challenge of histopathological heterogeneity and improve cross-tumor generalization.
• To enable unbiased learning from diverse pathological data.

Main Methods:

• Introduced a Cancer-Aware Attention module to model shared and cancer-specific patterns.
• Implemented an adversarial cancer regularization mechanism using mutual information minimization.
• Developed a hierarchical sample balancing strategy to mitigate data imbalances.

Main Results:

• Demonstrated significant improvements in generalization across diverse cancer types.
• Achieved scalable and unbiased multi-cancer WSI classification.
• Validated the framework on a uniquely constructed multi-cancer dataset.

Conclusions:

• The proposed framework offers a cohesive solution for unbiased multi-cancer WSI analysis.
• The Cancer-Aware Attention module and regularization techniques effectively handle heterogeneity.
• This approach provides a scalable foundation for advancing computational pathology across various cancers.