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An Introduction to Pathology Foundation Models.

Brandon Veremis1, Shengjia Chen2, Gabriele Campanella2

  • 1Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, Box 1194, New York, NY, 10029, USA. brandon.veremis@mountsinai.org.

Head and Neck Pathology
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Summary
This summary is machine-generated.

Foundation models, a type of machine learning, offer pathology insights without expert labeling. These transformer models are becoming vital for pathology research and potential clinical use.

Keywords:
Computer visionFoundation modelLarge language modelTransformerVision transformer

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Area of Science:

  • Artificial Intelligence in Medicine
  • Computational Pathology
  • Machine Learning for Healthcare

Background:

  • Foundation models represent a novel class of machine learning algorithms.
  • These models leverage large datasets and unsupervised learning, reducing the need for expert data labeling.
  • They excel at generating foundational representations of data patterns.

Purpose of the Study:

  • To provide a non-technical overview of transformer models for pathologists.
  • To explain the utility of foundation models in pathology.
  • To highlight released pathology foundation models and their research applications.

Main Methods:

  • The study offers a conceptual overview of transformer models.
  • It discusses the application of foundation models in pathology workflows.
  • Illustrative examples use head-and-neck cancer data within a publicly available foundation model.

Main Results:

  • Foundation models serve as an effective initial step in machine learning projects for pathology.
  • They generate internal data representations applicable to downstream tasks like tumor classification and biomarker prediction.
  • The use of foundation models can streamline pathology research by reducing reliance on manual data annotation.

Conclusions:

  • Foundation models are poised to increase in prevalence within pathology machine learning research and clinical practice.
  • Their ability to learn from vast datasets and minimize manual labeling offers significant advantages.
  • Understanding these models is crucial for pathologists engaging with AI in their field.