Diagnostic text-guided representation learning in hierarchical classification for pathological whole slide image.

Area of Science:

• Digital pathology
• Artificial intelligence in medicine
• Medical image analysis

Background:

• Digital imaging and AI-driven analysis of whole slide images (WSIs) are crucial for cancer diagnosis.
• Current methods often rely on slide-level labels due to high annotation costs, limiting exploration of complex pathological relationships.
• Advanced pathology tasks require more sophisticated approaches beyond basic representation learning.

Purpose of the Study:

• To introduce a hierarchical pathological image classification framework.
• To propose PathTree, a novel representation learning method for complex WSI analysis.
• To improve the accuracy and depth of AI-assisted cancer diagnosis using WSIs.

Main Methods:

• PathTree models multi-classification diseases as a binary tree structure, encoding professional pathological text descriptions.
• It utilizes a tree-like encoder to message information between text descriptions and image features.
• Slide-text similarity guides the aggregation of hierarchical representations, incorporating tree-specific losses to strengthen associations.

Main Results:

• PathTree demonstrated competitive performance against state-of-the-art methods on three challenging hierarchical classification datasets (lung, prostate, breast cancer).
• The method effectively handles the diversity of lesion types and their complex interrelationships.
• PathTree provides a new perspective for deep learning-based solutions in complex WSI classification.

Conclusions:

• PathTree offers a robust and effective approach for hierarchical pathological image classification.
• The method advances the application of AI in analyzing complex WSIs for cancer diagnosis.
• PathTree represents a significant step towards more sophisticated and accurate deep learning-assisted pathology.