Decoding pathology: the role of computational pathology in research and diagnostics.
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| Title: | Decoding pathology: the role of computational pathology in research and diagnostics. |
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| Authors: | Hölscher, David L., Bülow, Roman D. |
| Source: | Pflügers Archiv: European Journal of Physiology; Apr2025, Vol. 477 Issue 4, p555-570, 16p |
| Subject Terms: | DEEP learning, DIGITAL learning, INDIVIDUALIZED medicine, HISTOPATHOLOGY, PATHOLOGY |
| Abstract: | Traditional 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Pflügers Archiv: European Journal of Physiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
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