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

You might also read

Related Articles

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

Sort by
Same author

Isolation, genomic analysis, and evaluation of the novel lytic phage vB_Cf_HW01: potent antibiofilm activity and therapeutic efficacy against a clinical isolate of Citrobacter freundii.

Journal of applied microbiology·2026
Same author

A Prospective Head-to-Head Comparison of HER2-Targeted and 18F-FDG PET/CT for Detecting Axillary Lymph Node Metastases Among Newly Diagnosed HER2-Positive and HER2-Low Breast Cancer.

Clinical nuclear medicine·2026
Same author

Exploring distributed leadership and proactive change behavior in nursing: the roles of psychological safety and inclusive climate.

Scientific reports·2026
Same author

Case report: novel <i>DNAH11</i> compound heterozygous variants including an exon 30-54 duplication in a child with a highly suggestive primary ciliary dyskinesia phenotype.

Frontiers in genetics·2026
Same author

Dual epitope anti-LILRB4 synthetic T-cell receptor and antigen receptor (STAR)-T-cell therapy for relapsed/refractory acute myeloid leukemia.

Signal transduction and targeted therapy·2026
Same author

Glutathione-Mediated Biomimetic NO Activation with Coordination Capsules for NH<sub>3</sub> and α-Amino Acid Electrosynthesis.

Journal of the American Chemical Society·2026

Related Experiment Video

Updated: Sep 16, 2025

Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy
05:24

Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy

Published on: January 10, 2025

513

Medical Hyperspectral Image Feature Selection Framework Using Functional Data Analysis: Application to Membranous

Meng Lv, Shiyu Liu, Xiaoying Ma

    IEEE Journal of Biomedical and Health Informatics
    |July 4, 2025
    PubMed
    Summary

    This study introduces a novel feature selection framework for hyperspectral pathological diagnosis, achieving over 99% accuracy. The functional data analysis (FSFDA) method significantly reduces data dimensions while improving classification performance.

    More Related Videos

    High-definition Fourier Transform Infrared FT-IR Spectroscopic Imaging of Human Tissue Sections towards Improving Pathology
    11:05

    High-definition Fourier Transform Infrared FT-IR Spectroscopic Imaging of Human Tissue Sections towards Improving Pathology

    Published on: January 21, 2015

    33.4K
    Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis
    09:16

    Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

    Published on: June 18, 2020

    7.0K

    Related Experiment Videos

    Last Updated: Sep 16, 2025

    Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy
    05:24

    Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy

    Published on: January 10, 2025

    513
    High-definition Fourier Transform Infrared FT-IR Spectroscopic Imaging of Human Tissue Sections towards Improving Pathology
    11:05

    High-definition Fourier Transform Infrared FT-IR Spectroscopic Imaging of Human Tissue Sections towards Improving Pathology

    Published on: January 21, 2015

    33.4K
    Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis
    09:16

    Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

    Published on: June 18, 2020

    7.0K

    Area of Science:

    • Medical Imaging
    • Computational Pathology
    • Functional Data Analysis

    Background:

    • High-dimensional data processing poses challenges in hyperspectral pathological diagnosis.
    • Traditional discrete spectral representations limit the analysis of continuous spectral information.

    Purpose of the Study:

    • To develop a novel feature selection framework for hyperspectral pathological diagnosis.
    • To improve classification accuracy and reduce feature dimensions in pathological images.

    Main Methods:

    • A feature selection framework using functional data analysis (FSFDA) was developed.
    • Pixel spectra were modeled as continuous functions, preserving spectral continuity.
    • An adaptive spectral segmentation strategy and multi-criteria scoring mechanisms (FSFDA-S and FSFDA-U) were employed.

    Main Results:

    • The FSFDA framework achieved over 99% classification accuracy on a membranous nephropathy dataset.
    • Feature dimensions were reduced by 94.5%.
    • Effective identification of diagnostic bands for cross-modal data (human brain, white blood cells) was demonstrated.

    Conclusions:

    • FSFDA offers a robust solution for high-dimensional hyperspectral pathological data analysis.
    • The framework enhances diagnostic discriminability and maintains sparsity.
    • FSFDA demonstrates adaptive feature selection and cross-sample generalization capabilities.