Decoding pathology: the role of computational pathology in research and diagnostics
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Published:2024-08-03
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ISSN:0031-6768
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Container-title:Pflügers Archiv - European Journal of Physiology
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language:en
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Short-container-title:Pflugers Arch - Eur J Physiol
Author:
Hölscher David L.ORCID, Bülow Roman D.ORCID
Abstract
AbstractTraditional histopathology, characterized by manual quantifications and assessments, faces challenges such as low-throughput and inter-observer variability that hinder the introduction of precision medicine in pathology diagnostics and research. The advent of digital pathology allowed the introduction of computational pathology, a discipline that leverages computational methods, especially based on deep learning (DL) techniques, to analyze histopathology specimens. A growing body of research shows impressive performances of DL-based models in pathology for a multitude of tasks, such as mutation prediction, large-scale pathomics analyses, or prognosis prediction. New approaches integrate multimodal data sources and increasingly rely on multi-purpose foundation models. This review provides an introductory overview of advancements in computational pathology and discusses their implications for the future of histopathology in research and diagnostics.
Funder
Medizinische Fakultät, RWTH Aachen University RWTH Aachen University
Publisher
Springer Science and Business Media LLC
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