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A machine and human reader study on AI diagnosis model safety under attacks of adversarial images

Author

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  • Qianwei Zhou

    (University of Pittsburgh
    Zhejiang University of Technology
    Key Laboratory of Visual Media Intelligent Processing Technology of Zhejiang Province)

  • Margarita Zuley

    (University of Pittsburgh
    University of Pittsburgh Medical Center)

  • Yuan Guo

    (University of Pittsburgh
    Guangzhou First People’s Hospital, School of Medicine, South China University of Technology)

  • Lu Yang

    (University of Pittsburgh
    Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital)

  • Bronwyn Nair

    (University of Pittsburgh
    University of Pittsburgh Medical Center)

  • Adrienne Vargo

    (University of Pittsburgh
    University of Pittsburgh Medical Center)

  • Suzanne Ghannam

    (University of Pittsburgh
    University of Pittsburgh Medical Center)

  • Dooman Arefan

    (University of Pittsburgh)

  • Shandong Wu

    (University of Pittsburgh
    University of Pittsburgh
    University of Pittsburgh
    Intelligent Systems Program, University of Pittsburgh)

Abstract

While active efforts are advancing medical artificial intelligence (AI) model development and clinical translation, safety issues of the AI models emerge, but little research has been done. We perform a study to investigate the behaviors of an AI diagnosis model under adversarial images generated by Generative Adversarial Network (GAN) models and to evaluate the effects on human experts when visually identifying potential adversarial images. Our GAN model makes intentional modifications to the diagnosis-sensitive contents of mammogram images in deep learning-based computer-aided diagnosis (CAD) of breast cancer. In our experiments the adversarial samples fool the AI-CAD model to output a wrong diagnosis on 69.1% of the cases that are initially correctly classified by the AI-CAD model. Five breast imaging radiologists visually identify 29%-71% of the adversarial samples. Our study suggests an imperative need for continuing research on medical AI model’s safety issues and for developing potential defensive solutions against adversarial attacks.

Suggested Citation

  • Qianwei Zhou & Margarita Zuley & Yuan Guo & Lu Yang & Bronwyn Nair & Adrienne Vargo & Suzanne Ghannam & Dooman Arefan & Shandong Wu, 2021. "A machine and human reader study on AI diagnosis model safety under attacks of adversarial images," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-27577-x
    DOI: 10.1038/s41467-021-27577-x
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    References listed on IDEAS

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    1. Scott Mayer McKinney & Marcin Sieniek & Varun Godbole & Jonathan Godwin & Natasha Antropova & Hutan Ashrafian & Trevor Back & Mary Chesus & Greg S. Corrado & Ara Darzi & Mozziyar Etemadi & Florencia G, 2020. "International evaluation of an AI system for breast cancer screening," Nature, Nature, vol. 577(7788), pages 89-94, January.
    2. Scott Mayer McKinney & Marcin Sieniek & Varun Godbole & Jonathan Godwin & Natasha Antropova & Hutan Ashrafian & Trevor Back & Mary Chesus & Greg S. Corrado & Ara Darzi & Mozziyar Etemadi & Florencia G, 2020. "Addendum: International evaluation of an AI system for breast cancer screening," Nature, Nature, vol. 586(7829), pages 19-19, October.
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