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Related Experiment Video

Updated: Feb 28, 2026

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

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Improving Normal/Abnormal and Benign/Malignant Classifications in Mammography with ROI-Stratified Deep Learning.

Kenji Yoshitsugu1, Kazumasa Kishimoto2, Tadamasa Takemura1

  • 1Graduate School of Information Science, University of Hyogo, 7-1-28 Minatojima Minamimachi, Chuo-ku, Kobe-shi 650-0047, Hyogo, Japan.

Bioengineering (Basel, Switzerland)
|February 27, 2026
PubMed
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Improving Benign and Malignant Classifications in Mammography with ROI-Stratified Deep Learning.

Bioengineering (Basel, Switzerland)·2025
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Stratifying mammography images by region of interest (ROI) presence improves deep learning (DL) classification accuracy for breast cancer screening. This ROI-based approach shows greater utility with larger datasets, enhancing diagnostic performance.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Deep Learning (DL) is increasingly used for medical image analysis and diagnosis.
  • Mammography is a key tool for breast cancer screening, with ongoing research to improve its accuracy.

Purpose of the Study:

  • To evaluate if stratifying mammography images based on the presence or absence of a Region of Interest (ROI) enhances classification accuracy.
  • To compare the diagnostic performance of DL models with and without ROI-based stratification.

Main Methods:

  • Trained multiple DL models (ResNet, EfficientNet, SwinTransformer, ConvNeXt, MobileNet) independently on subgroups of mammography images (with/without ROI).
  • Integrated results from subgroup predictions for final classification.
  • Utilized publicly available datasets: VinDr., CDD-CESM, and DMID.
Keywords:
ROI-stratifieddeep learningmammographyregion of interest

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Related Experiment Videos

Last Updated: Feb 28, 2026

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Main Results:

  • ROI-based stratification improved classification accuracy for both normal-abnormal and benign-malignant cases.
  • The benefit of ROI stratification for diagnostic accuracy in mammography increased with larger dataset sizes.
  • Comparison with non-stratified predictions confirmed the enhanced utility of the ROI approach.

Conclusions:

  • Stratifying mammography images by ROI presence is a valuable technique for improving DL-based breast cancer screening accuracy.
  • The effectiveness of this stratification method is particularly pronounced in large-scale datasets.
  • This approach offers a promising strategy for enhancing diagnostic performance in mammography analysis.