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Machine learning in digital pathology.

Tomáš Brázdil, Vít Musil, Karel Štěpka

    Ceskoslovenska Patologie
    |August 5, 2025
    PubMed
    Summary
    This summary is machine-generated.

    Digital pathology uses machine learning (ML) and artificial intelligence (AI) for diagnostics. This overview covers data processing, challenges, and software solutions to accelerate clinical adoption of these learning systems.

    Keywords:
    Digital PathologyImage processingWhole-slide imagesartificial intelligencemachine learning

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    Area of Science:

    • Digital Pathology
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Digitalization of pathology is advancing rapidly.
    • Clinical implementation of machine learning (ML) and artificial intelligence (AI) systems lags behind research.
    • There is a need to bridge the gap between ML/AI development and clinical practice in pathology.

    Purpose of the Study:

    • To provide a comprehensive overview of developing and deploying learning systems in digital pathology.
    • To address technical challenges and potential pitfalls in processing digital pathology data.
    • To outline current and future developments in ML/AI for pathology.

    Main Methods:

    • Description of data characteristics in digital pathology (scanners, scanning, storage, transmission, quality control, annotations).
    • Review of software solutions for viewing digital slides and implementing diagnostic procedures with learning systems.
    • Explanation of common tasks, ML method modifications for large scans, and diagnostic applications.

    Main Results:

    • Current approaches to technical challenges in digital pathology data processing are presented.
    • Potential pitfalls in data processing are highlighted.
    • Existing software and diagnostic applications incorporating learning systems are outlined.

    Conclusions:

    • Understanding the nuances of digital pathology data is crucial for successful ML/AI implementation.
    • Addressing technical challenges can facilitate the adoption of learning systems.
    • Future developments include large foundational models and virtual staining, promising further advancements.