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 Experiment Video

Updated: Jan 14, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.3K

Distance Learning-Based Prototypical Network With Multi-Domain Adaptation for Few-Shot Hyperspectral Medical Image

Favour Ekong, Jun Zhou, Jing Wang

    IEEE Journal of Biomedical and Health Informatics
    |January 12, 2026
    PubMed
    Summary
    This summary is machine-generated.

    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

    RetVes segmentation: A pseudo-labeling and feature knowledge distillation optimization technique for retinal vessel channel enhancement.

    Computers in biology and medicine·2024
    Same author

    EVAE-Net: An Ensemble Variational Autoencoder Deep Learning Network for COVID-19 Classification Based on Chest X-ray Images.

    Diagnostics (Basel, Switzerland)·2022
    Same author

    Parallelistic Convolution Neural Network Approach for Brain Tumor Diagnosis.

    Diagnostics (Basel, Switzerland)·2022
    Same author

    Bayesian Depth-Wise Convolutional Neural Network Design for Brain Tumor MRI Classification.

    Diagnostics (Basel, Switzerland)·2022
    Same author

    [Surgical treatment of 402 consecutive cases for hilar cholangiocarcinoma: Chinese single center experience].

    Zhonghua wai ke za zhi [Chinese journal of surgery]·2007
    Same author

    Highly convergent route to cyclopeptide alkaloids: total synthesis of ziziphine N.

    Organic letters·2007
    Same journal

    AD-DAE: Alzheimer's Disease Progression Modeling with Unpaired Longitudinal MRI using Diffusion Auto-Encoders.

    IEEE journal of biomedical and health informatics·2026
    Same journal

    EEG Connectivity Signatures in Active vs. Passive Mental Fatigue Settings.

    IEEE journal of biomedical and health informatics·2026
    Same journal

    Privacy-Enhanced Vertical Federated Learning for Healthcare via Directional Noise and Subset Representations.

    IEEE journal of biomedical and health informatics·2026
    Same journal

    Multimodal Bidirectional Direct Preference Optimization and Instruction Fine-Tuning for Medical Image Understanding and Generation.

    IEEE journal of biomedical and health informatics·2026
    Same journal

    CT: A Controllable Transformer for Multi-Task TCM Facial Inspection.

    IEEE journal of biomedical and health informatics·2026
    Same journal

    Marfan Syndrome Prediction Via Graph Neural Networks on 3D Facial Cues.

    IEEE journal of biomedical and health informatics·2026
    See all related articles

    This study introduces a new hyperspectral imaging (HSI) classification method using distance learning and domain adaptation. It improves few-shot learning for medical diagnostics by addressing data scarcity and domain shifts, enhancing disease detection accuracy.

    Area of Science:

    • Medical imaging
    • Machine learning
    • Biomedical engineering

    Background:

    • Hyperspectral imaging (HSI) offers potential for medical diagnostics by analyzing spectral signatures.
    • Challenges include limited labeled data for training and domain shift across datasets, hindering generalization.

    Purpose of the Study:

    • To develop a novel distance-learning-based prototypical network for few-shot hyperspectral medical image classification.
    • To address data scarcity and domain shift issues in clinical HSI analysis.

    Main Methods:

    • Proposed a class-covariance-aware Mahalanobis metric within a prototypical network to adapt similarity measures.
    • Introduced a domain-aware adapter block for dynamic fusion of spectral-spatial representations and domain-specific characteristics.
    • Validated on skin dermoscopy, choledochal, and in-vivo brain HSI datasets.

    Related Experiment Videos

    Last Updated: Jan 14, 2026

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    3.3K

    Main Results:

    • The novel method demonstrated enhanced prototype robustness under label scarcity.
    • Significantly reduced misclassification compared to existing few-shot networks.
    • Achieved excellent performance across three diverse medical HSI datasets.

    Conclusions:

    • The proposed method effectively overcomes limitations of label scarcity and domain shift in medical HSI classification.
    • Paves the way for generalizable HSI solutions in clinical workflows and biomedical research.
    • Highlights the potential of advanced machine learning for precise disease detection using HSI.