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Enhancing Histopathological Image Classification Performance through Synthetic Data Generation with Generative

Jose L Ruiz-Casado1, Miguel A Molina-Cabello1,2, Rafael M Luque-Baena1,2

  • 1ITIS Software, University of Málaga, C/ Arquitecto Francisco Peñalosa, 18, 29010 Malaga, Spain.

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Summary
This summary is machine-generated.

This study addresses imbalanced breast cancer datasets for deep learning models. It explores using generative adversarial networks (GANs) for data augmentation to improve histopathological image classification performance.

Keywords:
breast cancerclassificationdata augmentationgenerative adversarial networkshistopathological images

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Area of Science:

  • Oncology
  • Medical Imaging
  • Computer Science

Background:

  • Breast cancer is a leading global cancer, necessitating accurate malignancy assessment via histopathological image analysis.
  • Deep learning models are increasingly used for image analysis but struggle with imbalanced datasets, leading to poor generalizability.
  • Traditional data augmentation techniques like translation or rotation may not suffice for small, imbalanced datasets.

Purpose of the Study:

  • To enhance the performance of deep learning models in classifying histopathological breast cancer images.
  • To investigate the efficacy of generative adversarial networks (GANs) for data augmentation in this context.
  • To overcome the limitations of traditional data augmentation and dataset downsampling for imbalanced medical image datasets.

Main Methods:

  • The study focuses on applying generative adversarial networks (GANs) for data augmentation.
  • These GAN-generated images are used to balance imbalanced histopathological breast cancer datasets.
  • The performance of models trained on GAN-augmented data is compared against traditional methods.

Main Results:

  • Generative adversarial networks (GANs) offer a promising approach to data augmentation for imbalanced datasets.
  • GAN-based augmentation can improve the generalizability and performance of deep learning models in histopathological image classification.
  • This method is particularly beneficial when dealing with limited or imbalanced medical imaging data.

Conclusions:

  • Generative adversarial networks (GANs) provide an effective alternative to traditional data augmentation techniques for imbalanced breast cancer datasets.
  • Utilizing GANs for data augmentation can significantly improve the accuracy and reliability of deep learning-based histopathological image analysis.
  • This research highlights the potential of advanced generative models in addressing critical challenges in medical AI.