RobMedNAS: searching robust neural network architectures for medical image synthesis.

Journal: Biomedical physics & engineering express
Published Date: Aug 23, 2024

Abstract

Investigating U-Net model robustness in medical image synthesis against adversarial perturbations, this study introduces RobMedNAS, a neural architecture search strategy for identifying resilient U-Net configurations. Through retrospective analysis of synthesized CT from MRI data, employing Dice coefficient and mean absolute error metrics across critical anatomical areas, the study evaluates traditional U-Net models and RobMedNAS-optimized models under adversarial attacks. Findings demonstrate RobMedNAS's efficacy in enhancing U-Net resilience without compromising on accuracy, proposing a novel pathway for robust medical image processing.

Authors

  • Jinnian Zhang
    Department of Statistics, University of Wisconsin, Madison, WI, USA.
  • Weijie Chen
  • Tanmayee Joshi
    Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, United States of America.
  • Meltem Uyanik
    Medical Physics, University of Wisconsin-Madison, Madison, United States of America.
  • Xiaomin Zhang
    Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Po-Ling Loh
  • Varun Jog
    Department of Pure Mathematics and Mathematical Statistics, Centre for Mathematical Sciences, University of Cambridge, Cambridge, UK.
  • Richard Bruce
    Radiology, University of Wisconsin-Madison, Madison, United States of America.
  • John Garrett
    Radiology, University of Wisconsin-Madison, Madison, United States of America.
  • Alan McMillan
    Department of Medical Physics, University of Wisconsin at Madison, Madison, WI, USA; Department of Radiology, University of Wisconsin at Madison, Madison, WI, USA; Deparment of Biomedical Engineering, University of Wisconsin at Madison, Madison, WI, USA.

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