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Related Experiment Video

Updated: Jun 14, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Multi-Scale Cross-Attention Multiple Instance Learning Network for Automated Classification of Colorectal Polyps.

Wisdom Ikezogwo1, Yongjun Liu2, Kareem Hosny2

  • 1Department of Computer Science and Engineering, University of Washington, Seattle, WA, USA.

Cancer Informatics
|June 9, 2026
PubMed
Summary

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This summary is machine-generated.

An artificial intelligence (AI) classifier effectively triages colorectal polyp specimens, achieving over 95% accuracy in identifying neoplastic versus nonneoplastic cases. This AI tool shows promise for improving pathology workflow and colorectal cancer screening.

Area of Science:

  • Digital Pathology
  • Artificial Intelligence in Medicine
  • Gastrointestinal Pathology

Background:

  • Gastrointestinal (GI) pathology services handle a high volume of colon polyp specimens, representing 40% of cases.
  • Updated colorectal cancer screening guidelines lower the screening age to 45, potentially increasing endoscopy procedures and specimen volume.
  • Developing an artificial intelligence (AI) classifier is crucial for efficient triage of colorectal polyp specimens.

Purpose of the Study:

  • To develop and evaluate an AI classifier for triaging colorectal polyp specimens.
  • To perform binary (neoplastic vs. nonneoplastic) and 12-way (final diagnosis) classifications.
  • To assess the AI classifier's performance on archived, routine clinical, and external datasets.

Main Methods:

Keywords:
artificial intelligence classifiercolonoscopydysplasia/neoplasia and colorectal cancermultiple instance learningwhole slide image

Related Experiment Videos

Last Updated: Jun 14, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

  • Retrospective analysis of 1191 colon polyp slides (2021-2023).
  • Training a multi-scale cross attention multiple instance learning (MsCAMIL) network using weakly-supervised transformer-based models.
  • Utilizing Leica Aperio scanners for slide digitization and evaluating performance metrics including F1-Score and accuracy.

    • Main Results:

    • The AI classifier achieved >95% accuracy and F1-score in binary classification for archived and routine clinical cases.
    • 12-way classification yielded lower F1 scores (74% archived, 57% routine).
    • Performance on an external dataset decreased to 86% accuracy, with variations noted across institutions.

    Conclusions:

    • MsCAMIL networks can serve as an efficient triage system in daily clinical workflow for colon polypectomy specimens.
    • The AI classifier demonstrates high specificity and accuracy (>95%) in binary classification for both archived and routine cases.
    • Multi-institutional collaborations are essential for further validation and broader clinical implementation.