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Updated: Dec 18, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Implementing Deep Learning Algorithms in Anatomic Pathology Using Open-source Deep Learning Libraries.

Ewen McAlpine1,2, Pamela Michelow1,2

  • 1Division of Anatomical Pathology, School of Pathology, University of the Witwatersrand.

Advances in Anatomic Pathology
|June 17, 2020
PubMed
Summary
This summary is machine-generated.

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This tutorial introduces pathologists to artificial intelligence (AI) and neural networks for histopathology image analysis. Learn to create, train, and evaluate AI models using Python and open-source libraries.

Area of Science:

  • Pathology
  • Computer Science
  • Bioinformatics

Background:

  • Artificial intelligence (AI) offers transformative potential in anatomic pathology.
  • Many pathologists lack familiarity with AI model development and evaluation.
  • A perceived skills gap hinders pathologists from engaging in AI research.

Purpose of the Study:

  • To provide an introductory tutorial for pathologists on creating, training, and evaluating AI models.
  • To demystify the process of developing simple neural networks for histopathology.
  • To familiarize pathologists with essential AI terminology and concepts.

Main Methods:

  • Utilizing Python programming language.
  • Employing open-source libraries: Keras, TensorFlow, PyTorch, and Detecto.

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  • Demonstrating model creation, training, and evaluation on histopathology datasets.

    • Main Results:

    • The tutorial illustrates practical steps for building and assessing AI models.
    • Pathologists can gain hands-on experience with AI development tools.
    • Common AI terms and concepts are explained in an accessible manner.

    Conclusions:

    • This resource empowers pathologists to understand and apply AI in their practice.
    • It bridges the knowledge gap regarding AI model development for histopathology.
    • Encourages greater pathologist involvement in AI-driven pathology research.