Technical note: Impact of tissue section thickness on accuracy of cell classification with a deep learning network

Microsections cut with DS in particular and thin sections in general are better suited for the intended cell classification system.

Authors

I Christiansen (ORCID: https://orcid.org/0000-0001-6115-2890)
T. Jensen (ORCID: https://orcid.org/0000-0001-5622-9347)
Rasmus Hartvig

Institutions

University of Copenhagen (DK)
Copenhagen University Hospital (DK)
Rigshospitalet (DK)

Publication Details

Journal: Journal of Pathology Informatics
Published: 2025-04-01
DOI: https://doi.org/10.1016/j.jpi.2025.100440
Primary Topic: Cell Image Analysis Techniques
Type: article
Field-Weighted Citation Impact: 0.00

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article

Technical note: Impact of tissue section thickness on accuracy of cell classification with a deep learning network

I Christiansen, T. Jensen, Rasmus Hartvig

Apr 1, 2025

Journal of Pathology Informatics

Cell Image Analysis Techniques

article

Technical note: Impact of tissue section thickness on accuracy of cell classification with a deep learning network

I Christiansen, T. Jensen, Rasmus Hartvig

April 1, 2025article en

Abstract

Microsections cut with DS in particular and thin sections in general are better suited for the intended cell classification system.

Journal of Pathology InformaticsVol. 17

University of Copenhagen (DK), Copenhagen University Hospital (DK), Rigshospitalet (DK)

Openalex Percentile: Top 94%

Cell Image Analysis Techniques

DOI Open Access PDF OpenAlex

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Technical note: Impact of tissue section thickness on accuracy of cell classification with a deep learning network — I Christiansen, T. Jensen, et al. · Journal of Pathology Informatics (2025) | TGRS Research Map | TGRS