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Updated: May 26, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Fast organ-of-origin classification for digital pathology quality control.

Witali Aswolinskiy1, John K L Wong1, Myroslav Zapukhlyak1

  • 1PAICON GmbH, Heidelberg, Germany.

Journal of Pathology Informatics
|May 25, 2026
PubMed
Summary
This summary is machine-generated.

A new deep learning model rapidly classifies the organ of origin from histopathology slides using thumbnails. This automated organ-of-origin classification aids quality control in digital pathology archives.

Keywords:
Deep learningDigital pathologyHistopathologyQuality control

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Area of Science:

  • Digital Pathology
  • Computational Pathology
  • Machine Learning in Histopathology

Background:

  • Digitizing large histopathology archives involves processing millions of whole-slide images, necessitating rapid validation.
  • Automated organ-of-origin classification can significantly accelerate quality control and identify mislabeled specimens in digital pathology workflows.

Purpose of the Study:

  • To develop and evaluate a deep learning model for rapid, automated classification of organ of origin from H&E-stained histopathology slides using low-resolution thumbnails.
  • To assess the model's performance on independent external cohorts and determine its potential for real-time quality control in large-scale digital pathology archives.

Main Methods:

  • A deep learning model was trained on 16,624 whole-slide image thumbnails from The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) archives, categorized into 14 organ classes.
  • The model was evaluated on two independent cohorts: a 5-class cohort (2,857 slides) and a 14-class cohort (12,348 slides).
  • Inference time was measured on an NVIDIA L4 GPU, averaging 0.2 seconds per slide.

Main Results:

  • The model achieved 90% balanced accuracy on the 5-class external cohort and 62% balanced accuracy on the 14-class external cohort.
  • For the 14-class cohort, high-confidence predictions (53% of data) achieved 74% balanced accuracy.
  • Manual review of high-confidence misclassifications suggested potential ground truth errors in some cases.
  • Mean inference time was exceptionally low at 0.2 seconds per slide.

Conclusions:

  • The developed deep learning model demonstrates high performance for automated organ-of-origin classification in digital pathology.
  • The model's rapid inference time makes it suitable for real-time, cost-effective quality control of large histopathology image archives.
  • This approach has the potential to improve the efficiency and accuracy of specimen validation in digital pathology workflows.