Histopathology language-image representation learning for fine-grained digital pathology cross-modal retrieval.

Area of Science:

• Digital pathology
• Computational oncology
• Medical image analysis

Background:

• Digital whole slide image (WSI) analysis is crucial for computer-aided cancer diagnosis.
• Content-based histopathological image retrieval (CBHIR) aids pathologists but faces challenges with large image sizes and manual annotation reliance.

Purpose of the Study:

• To develop a novel framework for fine-grained digital pathology cross-modal retrieval using language-image representation learning.
• To leverage paired diagnosis reports and WSIs for enhanced semantic understanding in histopathology.

Main Methods:

• An anchor-based WSI encoder extracts hierarchical region features.
• A prompt-based text encoder learns semantics from diagnosis reports.
• A multivariate cross-modal loss function trains the model at instance and region levels.

Main Results:

• The framework enables four types of retrieval tasks, supporting diagnostic needs.
• Experiments on in-house and public datasets demonstrate significant effectiveness.
• The proposed method shows advantages over existing histopathology retrieval techniques.

Conclusions:

• The developed framework offers a powerful tool for cross-modal retrieval in digital pathology.
• It effectively addresses limitations of current methods, enhancing diagnostic support.
• The approach facilitates more precise and efficient analysis of histopathological data.