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Exploiting the Vulnerability of Deep Learning-Based Artificial Intelligence Models in Medical Imaging: Adversarial Attacks

机译：利用医学成像中深层学习的人工智能模型的脆弱性：对抗攻击

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Hwiyoung Kim; Dae Chul Jung; Byoung Wook Choi;
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年度 2019
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1. On the vulnerability of deep learning to adversarial attacks for camera model identification [J] . Marra F., Gragnaniello D., Verdoliva L. Signal Processing. Image Communication: A Publication of the the European Association for Signal Processing . 2018,第期

机译：关于深度学习对相机模型识别的对抗攻击的脆弱性
2. Identify Susceptible Locations in Medical Records via Adversarial Attacks on Deep Predictive Models [J] . Mengying Sun, Fengyi Tang, Jinfeng Yi, SIGKDD explorations . 2018,第Udisk期

机译：通过对深刻预测模型的对抗攻击来识别医疗记录中的敏感位置
3. Universal adversarial attacks on deep neural networks for medical image classification [J] . Hokuto Hirano, Akinori Minagi, Kazuhiro Takemoto BMC Medical Imaging . 2021,第1期

机译：对医学图像分类深神经网络的普遍对抗攻击
4. Defending Deep Learning-Based Biomedical Image Segmentation from Adversarial Attacks: A Low-Cost Frequency Refinement Approach [C] . Qi Liu, Han Jiang, Tao Liu, International Conference on Medical Image Computing and Computer-Assisted Intervention . 2020

机译：从对抗性攻击中保护基于深度学习的生物医学图像分割：一种低成本的频率细化方法
5. Investigation of Deep Learning in Medical Imaging for Enhanced Workflow, Improved Diagnosis, and Explanatory Artificial Intelligence [D] . ?Crosby, Jennie Sandoz Marie 2020

机译：增强工作流程，改进诊断和解释性人工智能的医学成像深度学习调查
6. Vulnerability of deep neural networks for detecting COVID-19 cases from chest X-ray images to universal adversarial attacks [O] . Hokuto Hirano, Kazuki Koga, Kazuhiro Takemoto 2020

机译：深度神经网络检测Covid-19患者胸部X射线图像中的易受伤害性普遍普遍攻击
7. Adversarial Attack Vulnerability of Medical Image Analysis Systems: Unexplored Factors [O] . Gerda Bortsova, Cristina González-Gonzalo, Suzanne C. Wetstein, 2021

机译：医学图像分析系统的对抗攻击脆弱性：未开发的因素

Exploiting the Vulnerability of Deep Learning-Based Artificial Intelligence Models in Medical Imaging: Adversarial Attacks

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