Explainable AI Platform for Computational Pathology
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current computational pathology systems lack transparency and explainability, leading to pathologist skepticism and concerns about AI bias, safety, and understanding of underlying mechanisms, while deep learning methods fail to provide comprehensive high-level systems for image analysis.
Innovation Solution
The development of an explainable AI (xAI) platform, HistoMapr, which uses xAI to analyze whole slide images, providing real-time feedback, justifying its inferences with quantitated features, transparency, and causality, enabling pathologists to understand AI decisions and improve diagnostic accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If deep learning methods are used for image analysis, then analysis capability is improved, but transparency and explainability deteriorate
Solution Approach 1:
The patent introduces an intermediary layer between the deep learning model and the pathologist that captures and visualizes the decision-making process. This intermediary component records which image regions and features influenced the AI's diagnostic decisions, making the black-box deep learning process transparent without altering the underlying analysis capability.
Solution Approach 2:
The system implements feedback mechanisms that provide pathologists with explanatory information about AI decisions, including highlighted regions and reasoning traces. This feedback loop allows pathologists to understand and verify AI analyses while maintaining the sophisticated image processing capabilities of deep learning.
2Measurement precision
If AI systems are made more complex to improve diagnostic accuracy, then diagnostic accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex AI system into modular components: an image analysis module, a feature extraction module, a decision-making module, and an explanation generation module. This segmentation maintains high diagnostic accuracy through sophisticated processing while reducing perceived complexity by organizing functions into separate, manageable units.
Solution Approach 2:
An intermediary explanation layer is introduced that translates complex AI processing into understandable visual outputs without simplifying the underlying complex analysis. This mediator maintains diagnostic accuracy by preserving the full analytical capability while presenting results in a less complex, more interpretable format.
3Productivity
If AI provides automated diagnostic recommendations, then productivity is improved, but pathologist trust and acceptance deteriorate
Solution Approach 1:
The system provides continuous feedback to pathologists in the form of explainable diagnostic recommendations that show reasoning processes and supporting evidence. This feedback mechanism maintains productivity by automating analysis while building trust through transparency, allowing pathologists to understand and verify AI suggestions rather than simply accepting or rejecting black-box outputs.
Solution Approach 2:
The AI system serves itself by generating self-explanations that document its own reasoning process. This self-service capability allows the system to maintain high productivity through automated analysis while simultaneously building pathologist trust by providing self-generated explanatory information about its diagnostic recommendations.
Data Source
AI summary
Pathologists are adopting digital pathology for diagnosis, using whole slide images (WSIs). Explainable AI (xAI) is a new approach to AI that can reveal underlying reasons for its results. As such, xAI can promote safety, reliability, and accountability of machine learning for critical tasks such as pathology diagnosis. HistoMapr provides intelligent xAI guides for pathologists to improve the efficiency and accuracy of pathological diagnoses. HistoMapr can previews entire pathology cases' WSIs, identifies key diagnostic regions of interest (ROIs), determines one or more conditions associated with each ROI, provisionally labels each ROI with the identified conditions, and can triages them. The ROIs are presented to the pathologist in an interactive, explainable fashion for rapid interpretation. The pathologist can be in control and can access xAI analysis via a “why?” interface. HistoMapr can track the pathologist's decisions and assemble a pathology report using suggested, standardized terminology.