The Comparative Pathology Workbench: Interactive visual analytics for biomedical data

Area of Science:

• Digital Pathology
• Computational Pathology
• Bioinformatics

Background:

• Pathologists require robust tools for comparing histopathological images across diverse samples, cases, and species.
• Existing methods often lack dynamic, interactive comparison capabilities for integrated data analysis.

Purpose of the Study:

• To develop an interactive, web-browser-based system, the Comparative Pathology Workbench (CPW), for dynamic image and data comparison.
• To enhance collaborative analysis and interpretation of histopathological data among pathologists and other specialists.

Main Methods:

• Designed CPW as a visual analytics platform with an image-enabled 'spreadsheet' layout for organizing comparative data.
• Integrated features for comparing images at various magnifications, regions of interest, and analysis results (e.g., scRNA-seq).
• Implemented a Django-based web application with user management and discussion threads (via WordPress) for collaborative annotation.

Main Results:

• CPW allows direct, dynamic comparison of histopathological images and associated data, including image analysis outputs (QuPath, ImageJ).
• The platform supports shared access with configurable user permissions (read-only or edit).
• Integrated discussion threads enable collaborative interpretation and consensus building among experts globally.

Conclusions:

• The Comparative Pathology Workbench (CPW) provides a novel, accessible platform for advanced histopathological image and data comparison.
• CPW fosters interdisciplinary collaboration and improves diagnostic accuracy through shared analysis and discussion.
• The open-source nature and web-based accessibility of CPW promote widespread adoption in pathology research and diagnostics.