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Discovering anomalous patterns in large digital pathology images.

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  • 1IT, Analytics, and Operations, Mendoza College of Business, University of Notre Dame, Notre Dame, IN, 46556, U.S.A.

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A new statistical method, Hierarchical Linear Time Subset Scanning, efficiently detects cancer in large digital pathology slides. This tool aids pathologists by quickly identifying suspicious regions with high accuracy.

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Area of Science:

  • Digital Pathology
  • Computational Pathology
  • Medical Imaging Analysis

Background:

  • Medical imaging advances enable computer-aided diagnostic tools for analyzing digital pathology slides.
  • Existing methods struggle with large, raw digitized images, often requiring preselected small image regions.

Purpose of the Study:

  • To introduce Hierarchical Linear Time Subset Scanning (HLTSS), a novel statistical method for pattern detection in digital pathology.
  • To develop a method capable of analyzing massive, multiscale digital pathology slides to identify regions of interest for pathologists.

Main Methods:

  • HLTSS exploits the hierarchical structure of virtual microscopy data.
  • The method analyzes images at various resolution levels, starting coarse and progressing to granular resolutions to pinpoint anomalous subregions.
  • Demonstrated on digital slides of prostate biopsy samples.

Main Results:

  • The novel method accurately and quickly identifies cancerous locations on digital slides.
  • Achieved high accuracy in distinguishing benign from cancerous slides (ROC curve) and pinpointing malignant areas (spatial precision-recall curve).
  • Detected cancer regions within minutes, overcoming limitations of existing methods on large-scale images.

Conclusions:

  • HLTSS offers a significant advancement for analyzing large digitized pathology images.
  • The method effectively identifies regions of interest indicative of cancer, assisting pathologists in diagnosis.
  • This approach fills a critical gap in analyzing massive digital pathology data for improved diagnostic efficiency.