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

Laser-driven noncontact bubble transfer printing via a hydrogel composite stamp.

Proceedings of the National Academy of Sciences of the United States of America·2024
Same author

Differential perovskite hemispherical photodetector for intelligent imaging and location tracking.

Nature communications·2024
Same author

Real-world effectiveness of nirmatrelvir-ritonavir versus azvudine in hospitalized patients with COVID-19 during the omicron wave in Beijing: a multicenter retrospective cohort study.

BMC infectious diseases·2024
Same author

Morphological Profiling for Drug Discovery in the Era of Deep Learning.

ArXiv·2024
Same author

The epigenetic regulatory effect of histone acetylation and deacetylation on skeletal muscle metabolism-a review.

Frontiers in physiology·2023
Same author

A case of brain arteriovenous malformation treated by high-pressure cooker technique assisted with anhydrous alcohol embolization: A case report.

Medicine·2023

Related Experiment Video

Updated: Jul 12, 2025

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

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

Published on: December 15, 2014

22.5K

Multi-modality relation attention network for breast tumor classification.

Xiao Yang1, Xiaoming Xi1, Lu Yang1

  • 1School of Computer Science and Technology, Shandong Jianzhu University, Jinan, 250101, China.

Computers in Biology and Medicine
|October 20, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new deep learning model for breast cancer diagnosis using multi-modality imaging. The multi-modality relation attention network improves classification accuracy for diffusion-weighted imaging (DWI) and apparent dispersion coefficient (ADC) images.

Keywords:
Breast cancerDeep learningMedical image classificationMulti-modality fusionRelation learning

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K
Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

7.3K

Related Experiment Videos

Last Updated: Jul 12, 2025

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

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

Published on: December 15, 2014

22.5K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K
Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

7.3K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Breast cancer diagnosis relies on accurate image classification.
  • Multi-modality image fusion can enhance diagnostic performance.
  • Current fusion methods often overlook inter-modality correlations, limiting single-modality feature discrimination.

Purpose of the Study:

  • To propose a novel multi-modality relation attention network for improved breast tumor classification.
  • To leverage the complementary information from diffusion-weighted imaging (DWI) and apparent dispersion coefficient (ADC) images.
  • To enhance classification robustness and accuracy through consistent regularization.

Main Methods:

  • Development of a multi-modality relation attention network incorporating a novel attention module.
  • Exploration of correlation information between DWI and ADC modalities to improve feature discriminability.
  • Implementation of a consistency regularization module to ensure robust classification across modalities and reduce noise sensitivity.

Main Results:

  • The proposed network effectively classifies breast tumors using fused DWI and ADC images.
  • Experimental results show superior performance compared to existing multi-modality fusion techniques.
  • Achieved high performance metrics: AUC of 85.1%, accuracy of 86.7%, specificity of 83.3%, and sensitivity of 88.9%.

Conclusions:

  • The multi-modality relation attention network offers a significant advancement in breast tumor classification.
  • Integrating inter-modality correlations and consistent regularization enhances diagnostic accuracy and robustness.
  • The method demonstrates strong potential for improving breast cancer diagnosis through advanced image analysis.