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Imbalanced Breast Cancer Classification Using Transfer Learning.

Rishav Singh, Tanveer Ahmed, Abhinav Kumar

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |March 17, 2020
    PubMed
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    This study introduces a novel transfer learning framework to improve automated breast cancer detection from imbalanced histopathological images. The proposed method significantly outperforms existing techniques on a large dataset.

    Area of Science:

    • Medical Imaging
    • Computational Pathology
    • Artificial Intelligence in Medicine

    Background:

    • Accurate automated breast cancer detection is challenging, particularly with imbalanced datasets common in histopathology.
    • Existing methods struggle to achieve precise classification due to data imbalances.

    Purpose of the Study:

    • To develop and evaluate a transfer learning framework for improved histopathological image classification.
    • To address the challenge of imbalanced datasets in automated breast cancer detection.

    Main Methods:

    • Utilized transfer learning with the VGG-19 model as a base.
    • Applied learned knowledge from the ImageNet dataset to histopathological images.
    • Integrated state-of-the-art techniques to enhance classification performance.

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    Main Results:

    • The proposed framework demonstrated superior performance compared to existing literature on a large-scale dataset (277,524 images).
    • Achieved significant improvements in automated breast cancer detection accuracy.
    • Provided guidelines for transfer learning in imbalanced image classification.

    Conclusions:

    • The developed transfer learning framework offers a robust solution for accurate breast cancer detection from imbalanced histopathological data.
    • The findings suggest a promising direction for advancing automated diagnostic tools in oncology.
    • Numerical simulations provided valuable insights into optimizing transfer learning strategies for imbalanced datasets.