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

Search research articles

Related Experiment Video

Updated: Dec 24, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Automated mammographic mass detection using deformable convolution and multiscale features.

Junchuan Peng^1,2,3, Changyu Bao^1,2,3, Chuting Hu⁴

¹School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, 518060, Guangdong, People's Republic of China.

Medical & Biological Engineering & Computing

|April 17, 2020

Summary

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

A dish-to-biobank framework links β-cell nutrient-stress programs to genetic and dietary risk for Type 2 Diabetes.

bioRxiv : the preprint server for biology·2026

Same author

Human pancreatic progenitor organoids define genetic and epigenetic barriers to early PDAC transformation.

Developmental cell·2026

Same author

Event-triggered control of nonlinear networked control systems via a data-based representation.

ISA transactions·2026

Same author

Lipid asymmetry disruption by XKR8 orchestrates neutrophil extracellular trap formation and inhibits fungal infection.

Nature immunology·2026

Same author

Microbial Community Composition and Major Environmental Factors Influencing Changes in Different Vegetation Soils of Coastal Wetlands.

Microorganisms·2026

Same author

TADynFed: Dynamic modality-adaptive federated learning with tissue-aware disentanglement for cross-disease analysis.

Artificial intelligence in medicine·2026

Same journal

Reduced mechanical strength correlates with decreased elastin content in aortic intima-media tissue: association with dissection in human ascending aortas.

Medical & biological engineering & computing·2026

Same journal

How plaque morphology and stenosis severity govern stent-artery interaction and deployment outcomes: a computational study.

Medical & biological engineering & computing·2026

Same journal

Investigating a relation between amyloid beta plaque burden and accumulated neurotoxicity caused by amyloid beta oligomers.

Medical & biological engineering & computing·2026

Same journal

A robot-assisted eye positioning method with high precision and repeatability for ocular particle therapy: mechanical and geometric assessment.

Medical & biological engineering & computing·2026

Same journal

Enhanced puncture event detection for teleoperated needle insertion robotic system.

Medical & biological engineering & computing·2026

Same journal

Energy-efficient real-time 4-stage sleep classification at 10-second resolution.

Medical & biological engineering & computing·2026

See all related articles

This summary is machine-generated.

This study introduces an improved Faster R-CNN model for mammography lesion detection. The enhanced system, incorporating deformable convolution networks and NAS-FPN, achieves high accuracy in identifying breast cancer masses.

Area of Science:

Medical Imaging
Artificial Intelligence
Computer-Aided Diagnosis

Background:

Accurate detection of breast cancer lesions in mammography is crucial for early diagnosis and treatment.
Morphological variations in breast cancer present challenges for traditional computer-aided diagnosis (CAD) systems.
Robust feature extraction is essential for improving the precision of CAD systems.

Purpose of the Study:

To develop a novel mass detection CAD system for mammography using an enhanced Faster R-CNN architecture.
To improve the detection of lesions with diverse shapes and sizes by integrating deformable convolution networks (DCN).
To enhance the identification of small lesions through a neural architecture search-feature pyramid network (NAS-FPN) for multiscale feature integration.

Main Methods:

Keywords:

Deformable convolution network Faster R-CNN Mammography Mass detection Multiscale features

More Related Videos

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

Related Experiment Videos

Last Updated: Dec 24, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

The proposed system utilizes Faster R-CNN as its core architecture.
A deformable convolution network (DCN) was integrated into the backbone of Faster R-CNN to handle variations in lesion morphology.
A neural architecture search-feature pyramid network (NAS-FPN) was employed to create multiscale feature representations for improved detection of small lesions.

Main Results:

The enhanced CAD system demonstrated a high true positive rate of 0.9345 at 2.2805 false positives per image on the CBIS-DDSM dataset.
On the INbreast dataset, the system achieved a true positive rate of 0.9554 at 0.3829 false positives per image.
The integration of DCN and NAS-FPN significantly improved the model's ability to detect lesions of varying sizes and shapes.

Conclusions:

The proposed Faster R-CNN-based CAD system effectively detects breast cancer lesions in mammography images.
The use of deformable convolution networks and NAS-FPN enhances the system's robustness and accuracy, particularly for small or morphologically diverse lesions.
This advanced CAD system shows significant potential for clinical application in improving mammographic screening and diagnosis.