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

You might also read

Related Articles

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

Sort by
Same author

Enhancing the classification of spectrally similar land use/land cover classes using transfer learning in arid regions.

Scientific reports·2026
Same author

SNP-based prediction of schizophrenia using machine learning.

Bioinformatics advances·2026
Same author

SHAP-based explainable machine learning analysis of reward-related neural connectivity to predict preadolescent irritability.

Neuroimage. Reports·2026
Same author

Blood Metabolomic and Proteomic Signatures of Total, Healthy, and Unhealthy Low-Carbohydrate Diets in the UK Biobank.

Journal of the American Nutrition Association·2026
Same author

Benchmarking Distance Functions in Siamese Networks for Current and Prior Mammogram Image Analysis.

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine·2025
Same author

Unraveling the potential of diffusion models in small-molecule generation.

Drug discovery today·2025

Related Experiment Video

Updated: Nov 26, 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

3.2K

Convolutional neural network for automated mass segmentation in mammography.

Dina Abdelhafiz1,2, Jinbo Bi1, Reda Ammar1

  • 1Department of Computer Science and Engineering, University of Connecticut, Storrs, 06269, CT, USA.

BMC Bioinformatics
|December 10, 2020
PubMed
Summary

A new Vanilla U-Net model precisely segments mass lesions in mammograms, outperforming other deep learning models. This advancement aids radiologists in differentiating benign and malignant tumors for improved diagnostic accuracy.

Keywords:
Breast cancerComputer-aided detection (CAD)Convolutional neural networks (CNNs)Deep learning (DL)DetectionGround truth maps (GTMs)LocalizationMachine learning (ML)Mammograms (MGs)Pre-processingRegion growingSegNetSemantic pixel-wise segmentationU-NetVanilla U-Net

More Related Videos

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

656
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

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

Published on: August 30, 2013

43.3K

Related Experiment Videos

Last Updated: Nov 26, 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

3.2K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

656
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

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

Published on: August 30, 2013

43.3K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Mammogram (MG) image analysis for lesion segmentation is challenging.
  • Deep learning (DL) methods offer advanced solutions but require precise models.
  • Developing accurate models for mass lesion segmentation in MG images is crucial for early cancer detection.

Purpose of the Study:

  • To develop a novel deep learning model for precise segmentation of mass lesions in mammogram images.
  • To improve the accuracy and efficiency of automated lesion detection in mammography.
  • To enhance radiologists' ability to differentiate between benign and malignant lesions.

Main Methods:

  • A modified U-Net architecture, termed Vanilla U-Net, was developed for semantic segmentation.
  • The model combines low-level and high-level features to extract contextual information.
  • Training was performed on multiple large-scale public (CBIS-DDSM, BCDR-01, INbreast) and private mammogram databases.
  • Data augmentation techniques were employed to enhance model robustness and performance.

Main Results:

  • The Vanilla U-Net model demonstrated superior performance compared to state-of-the-art models like Faster R-CNN, FCN, SegNet, Dilated-Net, and original U-Net.
  • Achieved a mean test accuracy of 92.6% on digitized film-based and fully digitized MG images.
  • Obtained a mean Dice coefficient index (DI) of 0.951 and a mean Intersection over Union (IOU) of 0.909.
  • Data augmentation significantly improved mean DI from 0.922 to 0.951 and mean IOU from 0.856 to 0.909.

Conclusions:

  • The proposed Vanilla U-Net model enables precise pixel-wise segmentation of masses in mammograms.
  • The model's ability to capture multi-scale spatial context aids in differentiating lesion types.
  • Transfer learning, data augmentation, and architectural modifications led to superior performance over conventional and other DL models.