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18.11.2021 | Review

Artificial intelligence applied to breast pathology

verfasst von: Mustafa Yousif, Paul J. van Diest, Arvydas Laurinavicius, David Rimm, Jeroen van der Laak, Anant Madabhushi, Stuart Schnitt, Liron Pantanowitz

Erschienen in: Virchows Archiv | Ausgabe 1/2022

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Abstract

The convergence of digital pathology and computer vision is increasingly enabling computers to perform tasks performed by humans. As a result, artificial intelligence (AI) is having an astoundingly positive effect on the field of pathology, including breast pathology. Research using machine learning and the development of algorithms that learn patterns from labeled digital data based on “deep learning” neural networks and feature-engineered approaches to analyze histology images have recently provided promising results. Thus far, image analysis and more complex AI-based tools have demonstrated excellent success performing tasks such as the quantification of breast biomarkers and Ki67, mitosis detection, lymph node metastasis recognition, tissue segmentation for diagnosing breast carcinoma, prognostication, computational assessment of tumor-infiltrating lymphocytes, and prediction of molecular expression as well as treatment response and benefit of therapy from routine H&E images. This review critically examines the literature regarding these applications of AI in the area of breast pathology.

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Titel: Artificial intelligence applied to breast pathology
verfasst von: Mustafa Yousif
Paul J. van Diest
Arvydas Laurinavicius
David Rimm
Jeroen van der Laak
Anant Madabhushi
Stuart Schnitt
Liron Pantanowitz
Publikationsdatum: 18.11.2021
Verlag: Springer Berlin Heidelberg
Erschienen in: Virchows Archiv / Ausgabe 1/2022
Print ISSN: 0945-6317
Elektronische ISSN: 1432-2307
DOI: https://doi.org/10.1007/s00428-021-03213-3

Neu im Fachgebiet Pathologie

01.04.2024 | Forensische Traumatologie | CME

Springer Medizin

Artificial intelligence applied to breast pathology

Abstract

Neu im Fachgebiet Pathologie

Theoretische Grundlagen von Explosionen und Explosionsverletzungen

Auswirkungen vorgeschalteter Laborprozesse auf die Digitalisierung histologischer Schnittpräparate

Knochenmarkhistologie bei Zytopenien

Herausforderungen der Automation bei der quantitativen Auswertung von Leberbiopsien

Springer Medizin

Abstract

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