Corpus ID: 221818645

Reducing false-positive biopsies with deep neural networks that utilize local and global information in screening mammograms

@article{Wu2020ReducingFB,
  title={Reducing false-positive biopsies with deep neural networks that utilize local and global information in screening mammograms},
  author={Nan Wu and Z. Huang and Yiqiu Shen and Jungkyu Park and Jason Phang and T. Makino and S. Kim and Kyunghyun Cho and Laura Heacock and L. Moy and Krzysztof J Geras},
  journal={ArXiv},
  year={2020},
  volume={abs/2009.09282}
}
  • Nan Wu, Z. Huang, +8 authors Krzysztof J Geras
  • Published 2020
  • Computer Science, Engineering
  • ArXiv
  • Breast cancer is the most common cancer in women, and hundreds of thousands of unnecessary biopsies are done around the world at a tremendous cost. It is crucial to reduce the rate of biopsies that turn out to be benign tissue. In this study, we build deep neural networks (DNNs) to classify biopsied lesions as being either malignant or benign, with the goal of using these networks as second readers serving radiologists to further reduce the number of false positive findings. We enhance the… CONTINUE READING

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    References

    SHOWING 1-10 OF 45 REFERENCES
    Abnormality Detection in Mammography using Deep Convolutional Neural Networks
    • P. Xi, C. Shu, R. Goubran
    • Computer Science
    • 2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA)
    • 2018
    • 30
    • PDF
    Detecting and classifying lesions in mammograms with Deep Learning
    • 199
    • PDF
    Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening
    • 76
    • PDF
    Deep Learning to Distinguish Recalled but Benign Mammography Images in Breast Cancer Screening
    • 28
    • PDF
    MAMMO: A Deep Learning Solution for Facilitating Radiologist-Machine Collaboration in Breast Cancer Diagnosis
    • 12
    • PDF
    An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization
    • 4
    • PDF
    International evaluation of an AI system for breast cancer screening
    • 223
    • Highly Influential
    • PDF
    Applying Data-driven Imaging Biomarker in Mammography for Breast Cancer Screening: Preliminary Study
    • 21
    • PDF
    Automatic mass detection in mammograms using deep convolutional neural networks
    • 30
    • PDF
    Breast cancer histopathological image classification using Convolutional Neural Networks
    • 326
    • PDF