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New CRISP framework automates pathology WSI analysis

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) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New CRISP framework automates pathology WSI analysis

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Zahra Rahimi Afzal, Wataru Uegami, Saghir Alfasly, Saba Yasir, Judy C. Boughey, Matthew P. Goetz, Krishna R. Kalari, H. R. Tizhoosh ·

    CRISP -- Clustering-Based Redundancy-Reduced Instance Sampling for Pathology Case Representation and Retrieval

    arXiv:2605.24253v1 Announce Type: cross Abstract: Digital pathology archives increasingly contain multiple whole-slide images (WSIs) per case, capturing spatially distinct tumour regions and reflecting intrinsic morphological heterogeneity. However, most existing approaches rely …

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · H. R. Tizhoosh ·

    CRISP -- Clustering-Based Redundancy-Reduced Instance Sampling for Pathology Case Representation and Retrieval

    Digital pathology archives increasingly contain multiple whole-slide images (WSIs) per case, capturing spatially distinct tumour regions and reflecting intrinsic morphological heterogeneity. However, most existing approaches rely on a single pathologist-selected slide, thereby di…