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Related Experiment Video

Updated: Sep 22, 2025

Deep Neural Networks for Image-Based Dietary Assessment
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Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

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A deep learning framework to classify breast density with noisy labels regularization.

Hector Lopez-Almazan1, Francisco Javier Pérez-Benito1, Andrés Larroza1

  • 1Instituto Tecnológico de la Informática, Universitat Politècnica de València,Camino de Vera, s/n, 46022 València, Spain.

Computer Methods and Programs in Biomedicine
|May 20, 2022
PubMed
Summary

This study introduces RegL, a novel method for classifying digital mammograms into Breast Imaging Reporting and Data System (BI-RADS) categories. RegL achieves radiologist-level performance in breast density assessment, improving breast cancer risk prediction.

Keywords:
Breast densityDeep learningDense tissue classificationMammographyNoisy labels

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Last Updated: Sep 22, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Breast density on mammograms is a key breast cancer risk biomarker.
  • Current classification relies on expert radiologists using Breast Image and Data System (BI-RADS) categories.
  • Variability in expert labels introduces noise, impacting AI model performance.

Purpose of the Study:

  • To develop a reliable method for classifying digital mammograms into BI-RADS categories.
  • To enhance the accuracy of deep learning models for breast density assessment.
  • To address the challenge of noisy ground-truth labels in mammogram analysis.

Main Methods:

  • The study presents RegL (Labels Regularizer), a methodology incorporating image pre-processing for segmentation and quality enhancement.
  • A Confusion Matrix (CM)-CNN network architecture models individual radiologist variability.
  • The optimal pipeline was selected by comparing various pre-processing techniques and deep learning architectures.

Main Results:

  • An ensemble model combining five networks using the RegL methodology demonstrated superior performance.
  • The ensemble model achieved an accuracy of 0.85 and a kappa index of 0.71 on the test set.
  • The methodology was validated on a multi-center dataset of 1395 women's mammograms.

Conclusions:

  • The proposed RegL methodology achieves performance comparable to experienced radiologists in BI-RADS classification.
  • Pre-processing steps and modeling of radiologist labels improve the estimation of ground truth.
  • This approach offers a more reliable method for digital mammogram classification and breast cancer risk assessment.