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Related Experiment Video

Updated: May 5, 2026

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells
10:37

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

Published on: August 22, 2025

1.4K

LightweightUNet: Multimodal Deep Learning with GAN-Augmented Imaging Data for Efficient Breast Cancer Detection.

Hari Mohan Rai1, Joon Yoo1, Saurabh Agarwal2

  • 1School of Computing, Gachon University, Seongnam 13120, Republic of Korea.

Bioengineering (Basel, Switzerland)
|January 24, 2025
PubMed
Summary

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This summary is machine-generated.

A new LightweightUNet deep learning model offers accurate breast cancer detection with low computational cost. Combining mammogram and ultrasound images, with GAN-generated data, significantly improved performance, showing clinical potential.

Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Oncology

Background:

  • Breast cancer is a leading global cancer, necessitating early, automated, and precise detection methods.
  • Existing AI techniques for breast cancer detection often involve high computational costs and complexity.

Purpose of the Study:

  • To introduce an innovative LightweightUNet hybrid deep learning (DL) classifier for accurate breast cancer classification.
  • To develop a DL model with a low computational cost and adaptive capabilities for efficient breast cancer detection.

Main Methods:

  • A multimodal approach using 13,000 images from mammogram imaging (MGI) and ultrasound imaging (USI) from seven diverse sources.
  • Data preprocessing included resizing to 256x256 pixels and normalization using Box-Cox transformation.
Keywords:
GAN-augmentationLightweightUNetbreast cancer detectiondeep learninglightweight architecturemultimodal approachsynthetic dataset generation

Related Experiment Videos

Last Updated: May 5, 2026

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells
10:37

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

Published on: August 22, 2025

1.4K
  • Generative Adversarial Network (GAN) model StyleGAN3 was used to generate 10,000 synthetic ultrasound images to augment the smaller USI dataset. Experiments were conducted on real and real + GAN-augmented datasets using 5-fold cross-validation.

    • Main Results:

    • The LightweightUNet model achieved 86.87% accuracy on the real dataset without augmentation.
    • Performance significantly improved on the real + GAN-augmented dataset, reaching 96.35% accuracy.
    • The multimodal approach using LightweightUNet demonstrated substantial performance enhancements, including a 9.48% increase in accuracy.

    Conclusions:

    • The proposed LightweightUNet model offers an effective solution for breast cancer classification with reduced computational demands.
    • The integration of multimodal imaging and GAN-based data augmentation substantially boosts classification performance.
    • The model shows significant potential for practical application in clinical settings for improved breast cancer diagnosis.