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

Aggregates Classification01:29

Aggregates Classification

292
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
292
Force Classification01:22

Force Classification

1.1K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.1K

You might also read

Related Articles

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

Sort by
Same author

Fluoride varnish application and the temporal evolution of supragingival microbiota in children with differential caries risk.

Journal of oral microbiology·2026
Same author

Foundation Model-Based Zero-Shot Tissue Segmentation of Pathological Images via the Mixture of Local-to-Global Experts.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Î’-Lactoglobulin interacting with phlorizin alters its GI digestive peptide profile and synergistically enhances intestinal accessibility and MUC2 secretion on LS174T cells.

International journal of biological macromolecules·2026
Same author

Impact of SARS-CoV-2 infection history on the clinical features and outcomes of patients with recurrent pulmonary tuberculosis: a retrospective case-control study.

Annals of medicine·2026
Same author

Development and internal validation of a clinical prediction model for hemodialysis-related headache using LASSO and Boruta feature selection.

Scientific reports·2026
Same author

EfficientCovNet: Modeling the Pairwise Voxel Dependency for Brain ROI Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Embracing intra-class heterogeneity for semi-supervised medical image segmentation: From diversity to precision.

Medical image analysis·2026
Same journal

Real-time patient-specific microwave ablation zone prediction via a unified bioheat solver and MRI-informed perturbation learning.

Medical image analysis·2026
Same journal

Generative morphodynamic forecasting enables robust zero-shot volumetric medical segmentation.

Medical image analysis·2026
Same journal

ContiMorph: An unsupervised learning framework for cardiac motion tracking with time-continuous diffeomorphism.

Medical image analysis·2026
Same journal

MedP-CLIP: Medical CLIP with region-aware prompt integration.

Medical image analysis·2026
Same journal

Multi-organ guided diagnosis of mild cognitive impairment via hierarchical alignment and knowledge distillation.

Medical image analysis·2026
See all related articles

Related Experiment Video

Updated: May 16, 2025

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

2.6K

Image by co-reasoning: A collaborative reasoning-based implicit data augmentation method for dual-view CEUS

Peng Wan1, Haiyan Xue2, Shukang Zhang1

  • 1College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing 211106, China.

Medical Image Analysis
|April 2, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new implicit data augmentation method to improve machine learning for dual-view contrast-enhanced ultrasound (CEUS) classification. The approach enhances diagnostic accuracy for breast and liver cancers using limited clinical data.

Keywords:
Collaborative data augmentationDisease diagnosisDual-view contrast-enhanced ultrasound

More Related Videos

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

466
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.4K

Related Experiment Videos

Last Updated: May 16, 2025

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

2.6K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

466
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.4K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Ultrasound Technology

Background:

  • Limited dual-view contrast-enhanced ultrasound (CEUS) data hinders reliable machine learning model training.
  • Insufficient CEUS data fails to capture disease-specific texture variations, impacting model generalization.
  • Existing implicit data augmentation methods lack inter-view semantic consistency.

Purpose of the Study:

  • To propose a novel implicit data augmentation method for dual-view CEUS classification.
  • To address the challenge of inter-view semantic consistency in data augmentation.
  • To improve the performance of machine learning models using limited clinical CEUS data.

Main Methods:

  • Developed a sample-adaptive data augmentation technique with collaborative semantic reasoning across views.
  • Constructed a feature augmentation distribution for each ultrasound view, considering intra-class variance.
  • Ensured semantic consistency between augmented views by transferring plausible semantic changes.

Main Results:

  • Validated the method on dual-view CEUS datasets for breast and liver cancer.
  • Achieved superior mean diagnostic accuracy of 89.25% for breast cancer.
  • Achieved superior mean diagnostic accuracy of 95.57% for liver cancer.

Conclusions:

  • The proposed method effectively improves model performance with limited clinical CEUS data.
  • Implicit data augmentation with inter-view semantic consistency is crucial for dual-view CEUS analysis.
  • Demonstrated the potential for enhanced diagnostic accuracy in cancer detection using CEUS and AI.