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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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An Interactive Learning Framework for Scalable Classification of Pathology Images.

Michael Nalisnik1, David A Gutman2, Jun Kong3

  • 1Department of Computer Science and Mathematics, Emory University, Emory University School of Medicine, Atlanta, GA 30322.

Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data
|January 1, 2015
PubMed
Summary

This study introduces a web-based machine learning framework for analyzing pathology whole-slide images (WSIs). The system uses active learning to efficiently build classifiers, aiding in disease biology insights and patient outcome predictions.

Keywords:
biomedical image processinginteractive systemsmachine learningpathology

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Area of Science:

  • Computational pathology
  • Digital pathology
  • Machine learning in medicine

Background:

  • Advances in microscopy and genomics generate vast pathology data.
  • Whole-slide images (WSIs) offer high-resolution tissue visualization for diagnosis.
  • WSIs contain microanatomic features crucial for prognostic information.

Purpose of the Study:

  • To develop a web-based machine learning framework for pathology data analysis.
  • To enable rapid classifier building using an intuitive active learning process.
  • To minimize data labeling effort in computational pathology.

Main Methods:

  • Development of a web-based machine learning framework.
  • Implementation of an active learning process for classifier training.
  • Application of computational image analysis to extract morphologic features from WSIs.

Main Results:

  • The framework facilitates interaction with large WSI datasets.
  • Quantitative morphologic features are extracted and combined with clinical/genomic data.
  • Effectiveness demonstrated through quantification of glioma brain tumors.

Conclusions:

  • The developed framework aids in extracting insights from pathology data.
  • Machine learning and active learning accelerate the analysis of WSIs.
  • This approach enhances understanding of disease biology and patient outcomes.