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Challenges Developing Deep Learning Algorithms in Cytology.

Ewen David McAlpine1, Liron Pantanowitz2, Pamela M Michelow3

  • 1Division of Anatomical Pathology, School of Pathology, University of the Witwatersrand and National Health Laboratory Service, Johannesburg, South Africa, ewen.mcalpine@wits.ac.za.

Acta Cytologica
|November 2, 2020
PubMed
Summary
This summary is machine-generated.

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Artificial intelligence (AI) offers advantages in digital pathology and cytopathology but faces unique challenges. Addressing these, including data requirements and validation, is crucial for successful AI integration in cytology.

Area of Science:

  • Digital pathology and cytopathology
  • Artificial intelligence (AI) and deep learning applications

Background:

  • Digital pathology integration is increasing, offering AI/deep learning benefits.
  • Cytopathology presents unique challenges for AI implementation.

Purpose of the Study:

  • To review cytology-specific challenges in AI implementation.
  • To discuss global AI concerns applicable to cytology.

Main Methods:

  • Literature review of digital pathology and AI in cytology.
  • Analysis of cytology-specific technical and ethical hurdles.

Main Results:

  • Cytology requires digital implementation before AI, faces large file sizes and multiple stains.
  • Lack of annotated datasets, high computational resource needs, and validation/ethical concerns are key issues.
Keywords:
AlgorithmsArtificial intelligenceDeep learningDigital cytologyDigital pathology

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Conclusions:

  • AI integration in cytology requires addressing unique challenges like data, validation, ethics, and regulation.
  • Understanding digital pathology and algorithm development is vital for successful AI adoption in cytology.