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

Classification of Illness

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...

Mouse Models of Cancer Study

Mouse Models of Cancer Study

Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...

Cancer Survival Analysis

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...

You might also read

Related Articles

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

Sort by

Same author

Sodium-Glucose Cotransporter 2 Inhibitor Following Left Ventricular Assist Device Implantation: A Randomized Controlled Trial.

JACC. Heart failure·2026

Same author

Eisenmenger Syndrome Complicated by Hemorrhagic Hereditary Telangiectasia: Unique Intersection of Hemorrhage, Cyanosis, and Thrombosis.

JACC. Case reports·2026

Same author

Gene-Environment Interactions in Predicting Self-Harm: A Machine Learning Approach Using Explainable Artificial Intelligence.

Archives of suicide research : official journal of the International Academy for Suicide Research·2026

Same author

Virtual reality transition to underground: human responses across sequential spatial transitions.

Scientific reports·2026

Same author

Multi-modal AI for comprehensive breast cancer prognostication.

Nature communications·2026

Same author

Robust disease prognosis via diagnostic knowledge preservation: A sequential learning approach.

PloS one·2026

Same journal

AMD-Mamba: A Phenotype-Aware Multi-modal Framework for Robust AMD Prognosis.

Machine learning in medical imaging. MLMI (Workshop)·2026

Same journal

Pseudo-Rendering for Resolution and Topology-Invariant Cortical Parcellation.

Machine learning in medical imaging. MLMI (Workshop)·2025

Same journal

Probabilistic 3D Correspondence Prediction from Sparse Unsegmented Images.

Machine learning in medical imaging. MLMI (Workshop)·2024

Same journal

Privacy-preserving Federated Brain Tumour Segmentation.

Machine learning in medical imaging. MLMI (Workshop)·2024

Same journal

Robust Unsupervised Super-Resolution of Infant MRI via Dual-Modal Deep Image Prior.

Machine learning in medical imaging. MLMI (Workshop)·2024

Same journal

MoViT: Memorizing Vision Transformers for Medical Image Analysis.

Machine learning in medical imaging. MLMI (Workshop)·2024

See all related articles

Search research articles

Related Experiment Video

Updated: Dec 26, 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

Globally-Aware Multiple Instance Classifier for Breast Cancer Screening.

Yiqiu Shen¹, Nan Wu¹, Jason Phang¹

¹Center for Data Science, New York University, New York, USA.

Machine Learning in Medical Imaging. MLMI (Workshop)

|March 10, 2020

Summary

This summary is machine-generated.

A new deep learning model effectively classifies breast cancer lesions in mammography by analyzing both global and local image features. This approach achieves radiologist-level performance and aids in pinpointing suspicious findings.

Keywords:

Breast cancer screening Deep learning High-resolution image classification Neural networks Weakly supervised localization

More Related Videos

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Related Experiment Videos

Last Updated: Dec 26, 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

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

Medical image analysis
Artificial intelligence in healthcare
Radiology

Background:

Deep learning models for natural images are increasingly used in medical imaging.
Medical images present unique challenges like high resolution and small regions of interest.
Both global structure and local details are critical for accurate medical image interpretation.

Purpose of the Study:

To develop a novel neural network for breast cancer lesion classification.
To leverage both global and local image information for improved diagnostic accuracy.
To achieve radiologist-level performance in screening mammography interpretation.

Main Methods:

Proposed a neural network integrating global saliency maps and multiple local patches.
Utilized image-level labels for training the classification model.
Evaluated performance against a ResNet-based baseline.

Main Results:

The proposed model surpassed the ResNet-based baseline in performance.
Achieved radiologist-level accuracy in interpreting screening mammograms.
Generated pixel-level saliency maps for lesion localization, despite training on image-level labels.

Conclusions:

The novel deep learning approach effectively classifies breast cancer lesions using combined global and local features.
The model demonstrates potential for enhancing mammography interpretation and identifying malignant findings.
The ability to generate localization maps offers valuable insights for clinical decision-making.