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Updated: Sep 18, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Risk Classification of Low-Resolution Whole-Slide Thumbnail Images by Multi-dimensional Feature Reconstruction with

Cher-Wei Liang1,2, Yu-Chen Lee3, Yu-Yin Hsu1

  • 1School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei City, 24205, Taiwan.

Journal of Imaging Informatics in Medicine
|June 25, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an AI framework to screen malignant pathology slides from thumbnail whole-slide images (TWSIs). The AI system rapidly prioritizes high-risk cases, improving surgical pathology workflow efficiency.

Keywords:
Low-resolution thumbnail imagesMedical image classificationMedical image segmentationMulti-task deep learningWhole-slide pathology image

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Area of Science:

  • Digital Pathology
  • Artificial Intelligence in Medicine
  • Computational Pathology

Background:

  • Current surgical pathology workflows may delay high-risk cases due to registry order prioritization.
  • Delayed diagnosis of malignant lesions can negatively impact patient outcomes.

Purpose of the Study:

  • To develop and evaluate an AI-based framework for efficient screening and prioritization of malignant cases using thumbnail whole-slide images (TWSIs).
  • To improve the triage of surgical pathology specimens and enhance clinical decision-making.

Main Methods:

  • Developed an AI framework analyzing hematoxylin and eosin (H&E)-stained TWSIs.
  • Implemented image preprocessing, a multi-task deep learning network for segmentation and classification, and multi-dimensional feature reconstruction.
  • Evaluated the system on 334 TWSIs (100 benign, 234 malignant).

Main Results:

  • Achieved an average inference time of 2.33 ± 0.31 seconds per image.
  • Demonstrated high performance with 91.91% accuracy, 93.59% sensitivity, and 88.00% specificity.
  • Reported a false negative rate of 6.41%.

Conclusions:

  • AI-driven analysis of TWSIs can effectively expedite case triage in surgical pathology.
  • The proposed framework enhances the sorting and prioritization of specimens, potentially improving patient care.
  • This technology offers a promising approach to optimize pathology workflows and clinical decision support.