Adversarial Attack Vulnerability of Medical Image Analysis Systems: Unexplored Factors

[1]  Pan He,et al.  Adversarial Examples: Attacks and Defenses for Deep Learning , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[2]  Yarin Gal,et al.  Dropout Inference in Bayesian Neural Networks with Alpha-divergences , 2017, ICML.

[3]  Ronald M. Summers,et al.  ChestX-ray: Hospital-Scale Chest X-ray Database and Benchmarks on Weakly Supervised Classification and Localization of Common Thorax Diseases , 2019, Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics.

[4]  Dorin Comaniciu,et al.  Learning to recognize Abnormalities in Chest X-Rays with Location-Aware Dense Networks , 2018, CIARP.

[5]  Dan Boneh,et al.  Ensemble Adversarial Training: Attacks and Defenses , 2017, ICLR.

[6]  Seyed-Mohsen Moosavi-Dezfooli,et al.  Universal Adversarial Perturbations , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  Hayit Greenspan,et al.  An Adversarial Learning Approach to Medical Image Synthesis for Lesion Detection , 2018, IEEE Journal of Biomedical and Health Informatics.

[8]  M. Abràmoff,et al.  Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning. , 2016, Investigative ophthalmology & visual science.

[9]  Aleksander Madry,et al.  Robustness May Be at Odds with Accuracy , 2018, ICLR.

[10]  Mark Button,et al.  The Financial Cost of Healthcare Fraud 2015: What Data from Around the World Shows , 2010 .

[11]  James Bailey,et al.  Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systems , 2019, Pattern Recognit..

[12]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Max Welling,et al.  Rotation Equivariant CNNs for Digital Pathology , 2018, MICCAI.

[14]  Bram van Ginneken,et al.  A survey on deep learning in medical image analysis , 2017, Medical Image Anal..

[15]  James Bailey,et al.  Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets , 2020, ICLR.

[16]  Marco Eichelberg,et al.  Cybersecurity in PACS and Medical Imaging: an Overview , 2020, Journal of Digital Imaging.

[17]  Xiangyu Zhang,et al.  Attacks Meet Interpretability: Attribute-steered Detection of Adversarial Samples , 2018, NeurIPS.

[18]  Andrew Y. Ng,et al.  CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning , 2017, ArXiv.

[19]  Ara Darzi,et al.  The challenges of cybersecurity in health care: the UK National Health Service as a case study. , 2019, The Lancet. Digital health.

[20]  B. van Ginneken,et al.  Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study. , 2020, The Lancet. Oncology.

[21]  D. Chandler Seven Challenges in Image Quality Assessment: Past, Present, and Future Research , 2013 .

[22]  Wesley De Neve,et al.  Impact of Adversarial Examples on Deep Learning Models for Biomedical Image Segmentation , 2019, MICCAI.

[23]  Ghassan Hamarneh,et al.  Vulnerability Analysis of Chest X-Ray Image Classification Against Adversarial Attacks , 2018, MLCN/DLF/iMIMIC@MICCAI.

[24]  Seyed-Mohsen Moosavi-Dezfooli,et al.  DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[25]  Ananthram Swami,et al.  Practical Black-Box Attacks against Machine Learning , 2016, AsiaCCS.

[26]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[27]  Fabio Roli,et al.  Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning , 2018, CCS.

[28]  Subhashini Venugopalan,et al.  Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. , 2016, JAMA.

[29]  B. van Ginneken,et al.  Computer aided detection of tuberculosis on chest radiographs: An evaluation of the CAD4TB v6 system , 2019, Scientific Reports.

[30]  Yarin Gal,et al.  Understanding Measures of Uncertainty for Adversarial Example Detection , 2018, UAI.

[31]  David A. Wagner,et al.  Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples , 2018, ICML.

[32]  Allison M. Onken,et al.  Deep learning assessment of breast terminal duct lobular unit involution: Towards automated prediction of breast cancer risk , 2019, PloS one.

[33]  Ananthram Swami,et al.  Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks , 2015, 2016 IEEE Symposium on Security and Privacy (SP).

[34]  Ender Konukoglu,et al.  Injecting and removing suspicious features in breast imaging with CycleGAN: A pilot study of automated adversarial attacks using neural networks on small images. , 2019, European journal of radiology.

[35]  Michael I. Jordan,et al.  Theoretically Principled Trade-off between Robustness and Accuracy , 2019, ICML.

[36]  Ting Wang,et al.  Interpretable Deep Learning under Fire , 2018, USENIX Security Symposium.

[37]  M. Lenaz Health-care fraud and abuse. , 2009, Connecticut medicine.

[38]  Patrick D. McDaniel,et al.  Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples , 2016, ArXiv.

[39]  Aleksander Madry,et al.  Towards Deep Learning Models Resistant to Adversarial Attacks , 2017, ICLR.

[40]  Ananthram Swami,et al.  The Limitations of Deep Learning in Adversarial Settings , 2015, 2016 IEEE European Symposium on Security and Privacy (EuroS&P).

[41]  Jinfeng Yi,et al.  Is Robustness the Cost of Accuracy? - A Comprehensive Study on the Robustness of 18 Deep Image Classification Models , 2018, ECCV.

[42]  M. Abràmoff,et al.  Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices , 2018, npj Digital Medicine.

[43]  David A. Forsyth,et al.  SafetyNet: Detecting and Rejecting Adversarial Examples Robustly , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[44]  Andrew L. Beam,et al.  Adversarial Attacks Against Medical Deep Learning Systems , 2018, ArXiv.

[45]  William J Rudman,et al.  Healthcare fraud and abuse. , 2009, Perspectives in health information management.

[46]  David Wagner,et al.  Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods , 2017, AISec@CCS.

[47]  Dawn Xiaodong Song,et al.  Delving into Transferable Adversarial Examples and Black-box Attacks , 2016, ICLR.

[48]  Ender Konukoglu,et al.  Visual Feature Attribution Using Wasserstein GANs , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[49]  Kouichi Sakurai,et al.  One Pixel Attack for Fooling Deep Neural Networks , 2017, IEEE Transactions on Evolutionary Computation.

[50]  Kimin Lee,et al.  Using Pre-Training Can Improve Model Robustness and Uncertainty , 2019, ICML.

[51]  Andrew L. Beam,et al.  Adversarial attacks on medical machine learning , 2019, Science.

[52]  Ajmal Mian,et al.  Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey , 2018, IEEE Access.

[53]  Samy Bengio,et al.  Adversarial examples in the physical world , 2016, ICLR.

[54]  E. Finkelstein,et al.  Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes , 2017, JAMA.

[55]  Andrew H. Beck,et al.  Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer , 2017, JAMA.

[56]  Aleksander Madry,et al.  On Evaluating Adversarial Robustness , 2019, ArXiv.

[57]  Jonathon Shlens,et al.  Explaining and Harnessing Adversarial Examples , 2014, ICLR.

[58]  Oleg S. Pianykh,et al.  How Secure Is Your Radiology Department? Mapping Digital Radiology Adoption and Security Worldwide. , 2016, AJR. American journal of roentgenology.

[59]  Nicholas Carlini,et al.  Is AmI (Attacks Meet Interpretability) Robust to Adversarial Examples? , 2019, ArXiv.

[60]  Yara T. E. Lechanteur,et al.  Evaluation of a deep learning system for the joint automated detection of diabetic retinopathy and age‐related macular degeneration , 2019, Acta ophthalmologica.

[61]  B. van Ginneken,et al.  Computer aided detection of tuberculosis on chest radiographs: An evaluation of the CAD4TB v6 system , 2019, Scientific Reports.

[62]  Ara Darzi,et al.  Cybersecurity and healthcare: how safe are we? , 2017, British Medical Journal.

[63]  Joan Bruna,et al.  Intriguing properties of neural networks , 2013, ICLR.

[64]  David A. Wagner,et al.  Towards Evaluating the Robustness of Neural Networks , 2016, 2017 IEEE Symposium on Security and Privacy (SP).

[65]  Yang Song,et al.  PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples , 2017, ICLR.

[66]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[67]  S. Tsaftaris,et al.  Pseudo-healthy synthesis with pathology disentanglement and adversarial learning , 2020, Medical Image Anal..

[68]  Pushmeet Kohli,et al.  Adversarial Risk and the Dangers of Evaluating Against Weak Attacks , 2018, ICML.

[69]  Nassir Navab,et al.  Generalizability vs. Robustness: Adversarial Examples for Medical Imaging , 2018, ArXiv.