Introduction to Digital Image Analysis in Whole-slide Imaging: A White Paper from the Digital Pathology Association
By F. Aeffner, M. Zarella, Nathan Buchbinder, M. Bui, M. Goodman, D. Hartman, Giovanni M. Lujan, Mariam A. Molani, Anil V. Parwani, Kate Lillard and 4 more. Journal of Pathology Informatics, 2019
353 citations recorded by the source corpus.
The advent of whole-slide imaging in digital pathology has brought about the advancement of computer-aided examination of tissue via digital image analysis. Digitized slides can now be easily annotated and analyzed via a variety of algorithms. This study reviews the fundamentals of tissue image analysis and aims to provide pathologists with basic information regarding the features, applications, and general workflow of these new tools. The review gives an overview of the basic categories of software solutions available, potential analysis strategies, technical considerations, and general algorithm readouts. Advantages and limitations of tissue image analysis are discussed, and emerging concepts, such as artificial intelligence and machine learning, are introduced. Finally, examples of how digital image analysis tools are currently being used in diagnostic laboratories, translational research, and drug development are discussed.
Subfields: Artificial Intelligence, Biophysics, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/56cdceb390b77b05edcb892fb07e38eb6ad41cf0
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In Artificial Intelligence
In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- Artificial intelligence (AI) and big data in cancer and precision oncology (2020, background): “…of AI algorithm’s will then allow for the use of specific treatments optimised for each individual patient [48]. is made possible by the microscope which has been fitted with special high resolution cameras combined with optics and software to produce high quality diagnostic images [56,57].”
- Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association (2019, methodology): “This method is an iterative process where typically a few parameters are manually tuned, built into an algorithm, and often tested only on a region of the slide image [13].”