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

Classification of Bones01:18

Classification of Bones

12.8K
The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The...
12.8K

You might also read

Related Articles

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

Sort by
Same author

A unified multi-task framework enables interpretable chest radiograph analysis.

Med (New York, N.Y.)·2026
Same author

Acute changes in ankle dorsiflexor strength and fNIRS-Derived cortical activation following a single session of neuromuscular electrical stimulation in healthy older adults.

Frontiers in aging·2026
Same author

Machine learning for predicting surgical difficulty of laparoscopic total mesorectal excision for rectal cancer: integrating MR-based pelvimetry and peritoneal reflection.

Frontiers in medicine·2026
Same author

A disease-centric vision-language foundation model for precision oncology in kidney cancer.

Nature communications·2026
Same author

Pulmonary embolism associated with CRBN-targeting immunomodulatory drugs: a FAERS-based pharmacovigilance and mechanistic analysis.

Journal of thrombosis and thrombolysis·2026
Same author

MADCrowner: Margin Aware Dental Crown design with template deformation and refinement.

Medical image analysis·2026

Related Experiment Video

Updated: Mar 26, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

264

Multi-Instance Deep Learning: Discover Discriminative Local Anatomies for Bodypart Recognition.

Zhennan Yan, Yiqiang Zhan, Zhigang Peng

    IEEE Transactions on Medical Imaging
    |February 11, 2016
    PubMed
    Summary

    This study introduces a multi-stage deep learning framework for medical image classification. It automatically identifies discriminative local regions for accurate bodypart recognition without manual annotation.

    More Related Videos

    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    10.1K
    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

    3.7K

    Related Experiment Videos

    Last Updated: Mar 26, 2026

    Automated Joint Space Detection Improves Bone Segmentation Accuracy
    06:45

    Automated Joint Space Detection Improves Bone Segmentation Accuracy

    Published on: November 28, 2025

    264
    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    10.1K
    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

    3.7K

    Area of Science:

    • Medical Image Analysis
    • Computer Vision
    • Machine Learning

    Background:

    • Discriminative information for image recognition, including medical imaging, often resides in local image patches.
    • Identifying specific body parts from transversal slices relies on localized anatomical features, such as the mediastinum region.

    Purpose of the Study:

    • To develop a multi-stage deep learning framework for image classification, specifically for bodypart recognition in medical images.
    • To automatically discover discriminative and non-informative local image regions for improved classification accuracy.
    • To learn an image-level classifier leveraging these identified local regions.

    Main Methods:

    • A two-stage learning scheme involving a convolutional neural network (CNN) trained in a multi-instance learning fashion during a pre-train stage.
    • Extraction of discriminative and non-informative local patches from training slices using the pre-trained CNN.
    • A boosting stage where the pre-learned CNN is enhanced using the identified local patches for final image classification.

    Main Results:

    • The proposed framework successfully identifies discriminative local patches automatically, eliminating the need for manual annotation.
    • The CNN model, by focusing on discriminative local appearances, achieved higher accuracy compared to models relying on global image context.
    • Validation on synthetic and large-scale CT datasets demonstrated superior performance over state-of-the-art methods, including standard deep CNNs.

    Conclusions:

    • The developed multi-stage deep learning framework effectively performs bodypart recognition by automatically discovering and utilizing discriminative local image features.
    • This approach offers a significant advancement in medical image analysis by enhancing classification accuracy through intelligent patch selection.
    • The method's ability to learn without manual annotation makes it a valuable tool for large-scale medical image datasets.