Accurate and adversarially robust classification of medical images and ECG time-series with gradient-free trained sign activation neural networks

Zhibo Yang, Yanan Yang, Yunzhe Xue, Frank Y. Shih, Justin Ady, Usman Roshan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Adversarial attacks in medical AI imaging systems can lead to misdiagnosis and insurance fraud as recently highlighted by Finlayson et. al. in Science 2019. They can also be carried out on widely used ECG time-series data as shown in Han et. al. in Nature Medicine 2020. At the heart of adversarial attacks are imperceptible distortions that are visually and statistically undetectable but cause the machine learning model to misclassify data. Recent empirical studies have shown that a gradient-free trained sign activation neural network ensemble model requires a larger distortion than state of the art models. We apply them on medical data in this study as a potential solution to detect and deter adversarial attacks. We show on chest X-ray and histopathology images, and on two ECG datasets that this model requires a greater distortion to be fooled than full-precision, binary, and convolutional neural networks, and random forests. We show that adversaries targeting the gradient-free sign networks are visually distinguishable from the original data and thus likely to be detected by human inspection. Since the sign network distortions are higher we expect an automated method could be developed to detect and deter attacks in advance. Our work here is a significant step towards safe and secure medical machine learning.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020
EditorsTaesung Park, Young-Rae Cho, Xiaohua Tony Hu, Illhoi Yoo, Hyun Goo Woo, Jianxin Wang, Julio Facelli, Seungyoon Nam, Mingon Kang
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2456-2460
Number of pages5
ISBN (Electronic)9781728162157
DOIs
StatePublished - Dec 16 2020
Event2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020 - Virtual, Seoul, Korea, Republic of
Duration: Dec 16 2020Dec 19 2020

Publication series

NameProceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

Conference

Conference2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020
Country/TerritoryKorea, Republic of
CityVirtual, Seoul
Period12/16/2012/19/20

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Information Systems and Management
  • Medicine (miscellaneous)
  • Health Informatics

Keywords

  • ECG
  • X-ray
  • adversarial attack
  • gradient-free trained sign activation neural networks
  • histopathology
  • robust classification

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