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

Updates on intratumoral therapies in melanoma.

Cancer·2026
Same author

Beyond luminal stenosis: Epicardial adipose tissue volume reflects coronary plaque burden according to CAD-RADS 2.0.

European journal of radiology·2026
Same author

Dynamic breast magnetic resonance imaging features and pathological correlation of patients diagnosed with breast cancer under the age of 40.

Acta radiologica (Stockholm, Sweden : 1987)·2026
Same author

Comparative Analysis of Quantitative and Semiquantitative Ultrafast DCE-MRI, Morphological Findings, and ADC in Breast Lesion Characterization.

Academic radiology·2026
Same author

Is There a Safer Way for Thyroid Nodule Ablation: Comparative Study of Cooled and Uncooled Systems in Benign Thyroid Nodule Treatment.

Ultrasound quarterly·2026
Same author

Virtual Non-Iodine Coronary Calcium Scoring on Photon-Counting CT: Patient- and Plaque-Level Analysis.

Diagnostics (Basel, Switzerland)·2026

Related Experiment Video

Updated: May 21, 2025

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

6.6K

Annotation-efficient, patch-based, explainable deep learning using curriculum method for breast cancer detection in

Ozden Camurdan1, Toygar Tanyel2, Esma Aktufan Cerekci3

  • 1Department of Radiology, Acibadem Healthcare Group, Istanbul, Turkey.

Insights Into Imaging
|March 19, 2025
PubMed
Summary

This study developed an efficient deep learning (DL) model for breast cancer detection in mammograms. The DL model achieved improved performance and explainability using curriculum learning with limited, strongly labeled data.

Keywords:
Breast cancer detectionCurriculum learningDeep learningExplainable artificial intelligence (XAI)Mammography

More Related Videos

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

42.6K
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

22.3K

Related Experiment Videos

Last Updated: May 21, 2025

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

6.6K
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

42.6K
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

22.3K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Mammography is crucial for breast cancer detection.
  • Interpreting mammograms poses a logistical challenge due to increasing volumes.
  • Deep learning (DL) models offer potential but often require extensive annotated datasets.

Purpose of the Study:

  • To develop an efficient DL model for breast cancer detection in mammograms.
  • To utilize both weak (image-level) and strong (bounding box) annotations.
  • To provide explainable AI (XAI) using Grad-CAM and assess it with ground truth overlap ratio.

Main Methods:

  • A patch-based DL model was developed using curriculum learning, progressively increasing patch sizes.
  • The model was trained with varying levels of strong supervision (0-100%) on 1976 mammograms.
  • Performance was evaluated on an internal dataset and an external dataset of 4276 mammograms.

Main Results:

  • Curriculum learning models (20-100% strong labels) outperformed the baseline model (0% strong labels) in F1 scores.
  • F1 scores improved from 80.55% (baseline) to 83.95% (curriculum 100) on the internal dataset.
  • Similar performance trends were observed on the external dataset, with F1 scores ranging from 74.65% to 78.73%.

Conclusions:

  • Training DL models with curriculum learning and a patch-based approach is effective, even with limited strongly annotated data.
  • This method offers satisfactory performance and explainability (XAI), addressing DL's data requirements and "black-box" nature.
  • The approach shows promise for deploying DL in large-scale mammography screening programs.