Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

5.6K
Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...
5.6K
Three-Dimensional Microscopy in Microbiology01:28

Three-Dimensional Microscopy in Microbiology

328
Three-dimensional imaging techniques are essential in cell biology, allowing researchers to visualize intricate cellular structures with high resolution. Two prominent methods, Differential Interference Contrast Microscopy (DIC) and Confocal Scanning Laser Microscopy (CSLM), provide distinct advantages for imaging live and thick specimens, respectively.Differential Interference Contrast MicroscopyDIC microscopy enhances contrast in transparent, unstained samples by converting phase...
328

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Rethinking foundation models in pathology.

Nature biomedical engineering·2026
Same author

Artificial intelligence modelling in grading breast phyllodes tumours.

Histopathology·2026
Same author

Foreword.

Mayo Clinic proceedings. Digital health·2026
Same author

Safety and Efficacy of Tumor-Infiltrating Lymphocyte Therapy with Reduced-Dose Lymphodepleting Conditioning in High-Risk Metastatic Melanoma Patients.

Transplantation and cellular therapy·2025
Same author

Self-assembly of lanthanide-based single-ion magnets (SIMs) into 1D networks <i>via</i> Re(IV)-based metalloligands.

Dalton transactions (Cambridge, England : 2003)·2025
Same author

Magnetic Resonance Imaging for Distinguishing Perianal Hidradenitis Suppurativa from Fistulizing Crohn Disease.

Dermatology (Basel, Switzerland)·2025

Related Experiment Video

Updated: Sep 20, 2025

Multiplexed Barcoding Image Analysis for Immunoprofiling and Spatial Mapping Characterization in the Single-Cell Analysis of Paraffin Tissue Samples
08:18

Multiplexed Barcoding Image Analysis for Immunoprofiling and Spatial Mapping Characterization in the Single-Cell Analysis of Paraffin Tissue Samples

Published on: April 7, 2023

1.8K

Multi-Magnification Image Search in Digital Pathology.

Maral Rasoolijaberi, Morteza Babaei, Abtin Riasatian

    IEEE Journal of Biomedical and Health Informatics
    |June 10, 2022
    PubMed
    Summary

    This study explores how magnification affects digital pathology image search. A multi-magnification approach improves search accuracy for tumor subtypes by combining different views, enhancing diagnostic capabilities.

    More Related Videos

    Expanding the Comprehension of the Tumor Microenvironment using Mass Spectrometry Imaging of Formalin-Fixed and Paraffin-Embedded Tissue Samples
    06:47

    Expanding the Comprehension of the Tumor Microenvironment using Mass Spectrometry Imaging of Formalin-Fixed and Paraffin-Embedded Tissue Samples

    Published on: June 29, 2022

    2.3K
    Author Spotlight: Multiplex Immunofluorescence Combined with Spatial Image Analysis for the Clinical and Biological Assessment of the Tumor Microenvironment
    06:05

    Author Spotlight: Multiplex Immunofluorescence Combined with Spatial Image Analysis for the Clinical and Biological Assessment of the Tumor Microenvironment

    Published on: June 2, 2023

    8.2K

    Related Experiment Videos

    Last Updated: Sep 20, 2025

    Multiplexed Barcoding Image Analysis for Immunoprofiling and Spatial Mapping Characterization in the Single-Cell Analysis of Paraffin Tissue Samples
    08:18

    Multiplexed Barcoding Image Analysis for Immunoprofiling and Spatial Mapping Characterization in the Single-Cell Analysis of Paraffin Tissue Samples

    Published on: April 7, 2023

    1.8K
    Expanding the Comprehension of the Tumor Microenvironment using Mass Spectrometry Imaging of Formalin-Fixed and Paraffin-Embedded Tissue Samples
    06:47

    Expanding the Comprehension of the Tumor Microenvironment using Mass Spectrometry Imaging of Formalin-Fixed and Paraffin-Embedded Tissue Samples

    Published on: June 29, 2022

    2.3K
    Author Spotlight: Multiplex Immunofluorescence Combined with Spatial Image Analysis for the Clinical and Biological Assessment of the Tumor Microenvironment
    06:05

    Author Spotlight: Multiplex Immunofluorescence Combined with Spatial Image Analysis for the Clinical and Biological Assessment of the Tumor Microenvironment

    Published on: June 2, 2023

    8.2K

    Area of Science:

    • Digital pathology
    • Computational pathology
    • Medical image analysis

    Background:

    • Digital pathology archives enable image search for clinical and research purposes.
    • Pathologists use varying magnifications to examine tissue morphology, a workflow not fully replicated by current AI search methods.
    • Existing AI image search methods often lack regional annotations and may not leverage multi-magnification information.

    Purpose of the Study:

    • To investigate the impact of magnification levels on content-based image search in digital pathology.
    • To propose and evaluate a multi-magnification image representation for AI-enabled image search.
    • To bridge the gap between conventional pathology workflows and AI-driven search functionalities.

    Main Methods:

    • Investigated multiple magnification levels (20×, 10×, 5×) and their combinations for digital pathology image search.
    • Developed two approaches for combining magnification levels: single-vector and multi-vector deep feature representations.
    • Evaluated the proposed framework on a subset of The Cancer Genome Atlas (TCGA) repository without relying on regional annotations.

    Main Results:

    • Cell-level information at high magnification is crucial for diagnostic image search.
    • Lower magnification information can enhance search performance for specific tumor types.
    • The multi-magnification approach demonstrated up to an 11% F1-score improvement for urinary tract and brain tumor subtypes compared to single-magnification search.

    Conclusions:

    • Multi-magnification image representation is effective for enhancing content-based image search in digital pathology.
    • The proposed framework, applicable to millions of unlabelled whole slide images, offers a practical advancement for AI in pathology.
    • Combining different magnification levels better mimics the pathologist's examination process, leading to improved diagnostic search accuracy.