CRISP -- Clustering-Based Redundancy-Reduced Instance Sampling for Pathology Case Representation and Retrieval
CRISP is an unsupervised machine learning framework that automates the analysis of multiple whole-slide images (WSIs) in digital pathology by selectively sampling informative patches across all slides in a case rather than relying on a single pathologist-selected slide. The approach matches or exceeds current clinical practice for breast cancer diagnosis and retrieval while eliminating subjective slide selection and reducing computational burden.
CRISP addresses a fundamental inefficiency in digital pathology workflows where clinical archives contain multiple WSIs per case but existing analysis methods rely on single, manually-selected slides. This limitation discards potentially critical diagnostic information distributed across tumor regions. The framework employs a two-stage clustering approach that first reduces within-slide redundancy, then applies intelligent sampling across all available slides to construct representative case-level representations without exhaustive gigapixel image processing.
The innovation builds on broader trends in computational pathology toward automating manual slide selection and leveraging multimodal data. Digital pathology adoption has accelerated as institutions digitize archives, yet workflow automation remains limited. Most systems still depend on pathologist expertise to identify diagnostically relevant slides, creating bottlenecks and introducing subjectivity.
CRISP's demonstrated performance on Mayo Clinic breast cancer datasets holds significant implications for clinical operations and research infrastructure. By automating case-level analysis, the framework reduces pathologist burden while improving consistency and potentially capturing morphological heterogeneity currently missed. For digital pathology vendors and healthcare providers, this represents a pathway toward efficient, scalable case processing that maintains or improves diagnostic accuracy.
The framework's direct compatibility with retrieval indexing enables practical deployment in existing clinical search systems. Future validation across additional cancer types and institutional datasets will determine whether CRISP becomes standard practice. Key questions involve generalization to rare tumors, integration with AI diagnostic models, and regulatory pathway for clinical deployment.
- →CRISP automates multi-slide analysis in digital pathology, eliminating subjective single-slide selection by pathologists
- →The clustering-based approach matches or exceeds current clinical practice on breast cancer diagnosis and retrieval tasks
- →The framework reduces computational burden by selectively sampling representative patches rather than processing entire gigapixel images
- →Case-level heterogeneity is preserved while avoiding exhaustive processing, enabling practical deployment in existing clinical workflows
- →Automation of WSI selection potentially unlocks diagnostically relevant information currently overlooked in multi-slide cases