Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Imaging Studies II: Ultrasonography

Imaging Studies II: Ultrasonography

IntroductionUltrasonography, or renal ultrasound, is a noninvasive medical imaging technique that uses high-frequency sound waves to visualize the kidneys, ureters, bladder, and surrounding tissues.Indications for Urinary System UltrasonographyUrinary system ultrasonography is indicated in various clinical scenarios, such as:Kidney Stones (Urolithiasis): To detect and monitor the size and presence of kidney or urinary tract stones.Hydronephrosis: To assess the dilation of the renal pelvis and...

You might also read

Related Articles

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

Sort by

Same author

Applications of Artificial Intelligence in Urodynamic Data Interpretation: A Narrative Review.

Neurourology and urodynamics·2026

Same author

Optimal Selection of the Effective Echo Time (TE<sub>eff</sub>) for T2-Weighted Fast-Spin-Echo MRI of the Prostate at 3.0 T: Effect on Lesion Conspicuity.

Journal of magnetic resonance imaging : JMRI·2026

Same author

BWS-Net: An Optimal Deep Learning Architecture for the Anterior Bladder Wall Segmentation using Ultrasound Imaging.

IEEE journal of biomedical and health informatics·2026

Same author

Treatment Monitoring of Carpal Tunnel Syndrome Using Shear Wave Elastography: A Pilot In Vivo Study.

Cureus·2026

Same author

Fertility Preservation in Female Cancer Patients: Comprehensive Clinical, Surgical, and Radiologic Guide for Radiologists.

Radiographics : a review publication of the Radiological Society of North America, Inc·2026

Same author

Deep learning-based classification of thyroid nodules using uncertainty-aware multi-modal ultrasound imaging.

Scientific reports·2026

Search research articles

Related Experiment Video

Updated: Jul 16, 2026

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

Published on: April 28, 2022

Variational mode directed deep learning framework for breast lesion classification using ultrasound imaging.

Manali Saini¹, Sara Hassanzadeh¹, Bushira Musa²

¹Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN, 55905, USA.

Scientific Reports

|April 24, 2025

Summary

This study introduces a new AI framework for breast cancer detection using ultrasound. It improves accuracy and explainability in classifying lesions, offering a more efficient and reliable diagnostic tool.

Keywords:

Convolutional layers Deep learning Mixed pooling Ultrasound Variational mode decomposition

More Related Videos

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

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

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

Published on: November 30, 2022

Related Experiment Videos

Last Updated: Jul 16, 2026

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

Published on: April 28, 2022

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

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

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

Published on: November 30, 2022

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Breast cancer is a leading cause of death in women, necessitating early detection.
Current deep learning methods for ultrasound-based breast lesion detection face challenges with explainability, segmentation, and computational cost.

Purpose of the Study:

To develop a novel ultrasound-based breast lesion classification framework.
To enhance the explainability and efficiency of deep learning models for breast cancer detection.

Main Methods:

Utilized two-dimensional variational mode decomposition (2D-VMD) to extract self-explanatory features.
Employed a convolutional neural network (CNN) with mixed pooling and attention mechanisms guided by 2D-VMD features.
Evaluated the framework on public and in-house breast ultrasound datasets without requiring lesion segmentation.

Main Results:

Achieved high classification accuracies: 98% and 93% on two public datasets, and 89% on an in-house dataset.
Demonstrated improved areas under the ROC (5%) and precision-recall (10%) curves.
Showcased significant computational efficiency with reduced floating-point operations compared to existing methods.

Conclusions:

The proposed 2D-VMD-guided CNN framework offers a highly accurate, explainable, and computationally efficient solution for breast lesion classification.
This approach overcomes limitations of existing methods by providing interpretable features and eliminating the need for segmentation.
The framework holds promise for improving early breast cancer detection and reducing mortality rates.