Researchers have developed CRISP, an unsupervised framework for analyzing multiple whole-slide images (WSIs) in digital pathology. This two-stage system first reduces redundancy within individual WSIs and then uses clustering to select a representative set of patches for the entire case. CRISP aims to capture case-level heterogeneity and serve as a retrieval index, potentially improving diagnosis and treatment planning by utilizing information currently overlooked in multi-WSI cases. AI
IMPACT Automates analysis of multiple pathology slides, potentially improving diagnostic accuracy and treatment planning by leveraging overlooked data.
RANK_REASON The cluster contains an academic paper detailing a new methodology.
Read on arXiv cs.IR (Information Retrieval) →
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