Literature DB >> 34521285

Whole-slide imaging, tissue image analysis, and artificial intelligence in veterinary pathology: An updated introduction and review.

Aleksandra Zuraw1, Famke Aeffner2.   

Abstract

Since whole-slide imaging has been commercially available for over 2 decades, digital pathology has become a constantly expanding aspect of the pathology profession that will continue to significantly impact how pathologists conduct their craft. While some aspects, such as whole-slide imaging for archiving, consulting, and teaching, have gained broader acceptance, other facets such as quantitative tissue image analysis and artificial intelligence-based assessments are still met with some reservations. While most vendors in this space have focused on diagnostic applications, that is, viewing one or few slides at a time, some are developing solutions tailored more specifically to the various aspects of veterinary pathology including updated diagnostic, discovery, and research applications. This has especially advanced the use of digital pathology in toxicologic pathology and drug development, for primary reads as well as peer reviews. It is crucial that pathologists gain a deeper understanding of digital pathology and tissue image analysis technology and their applications in order to fully use these tools in a way that enhances and improves the pathologist's assessment as well as work environment. This review focuses on an updated introduction to the basics of digital pathology and image analysis and introduces emerging topics around artificial intelligence and machine learning.

Entities:  

Keywords:  artificial intelligence; deep learning; digital pathology; image analysis; machine learning; veterinary; whole-slide imaging

Mesh:

Year:  2021        PMID: 34521285     DOI: 10.1177/03009858211040484

Source DB:  PubMed          Journal:  Vet Pathol        ISSN: 0300-9858            Impact factor:   2.221


  1 in total

1.  Multimodal Approach of Optical Coherence Tomography and Raman Spectroscopy Can Improve Differentiating Benign and Malignant Skin Tumors in Animal Patients.

Authors:  Mindaugas Tamošiūnas; Oskars Čiževskis; Daira Viškere; Mikus Melderis; Uldis Rubins; Blaž Cugmas
Journal:  Cancers (Basel)       Date:  2022-06-07       Impact factor: 6.575

  1 in total

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