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    Regularized Adversarial Training (RAT) for Robust Cellular Electron Cryo Tomograms Classification

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    2019_adversarial_attack_bibm_B665_ddl190817.pdf
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    Description:
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    Type
    Conference Paper
    Authors
    Wu, Xindi
    Mao, Yijun
    Wang, Haohan
    Zeng, Xiangrui
    Gao, Xin cc
    Xing, Eric P.
    Xu, Min
    KAUST Department
    Computational Bioscience Research Center (CBRC)
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Structural and Functional Bioinformatics Group
    KAUST Grant Number
    FCC/1/1976-18
    FCC/1/1976-23
    FCC/1/1976-26
    BAS/1/1624
    FCC/1/1976-25
    Date
    2020-02-07
    Submitted Date
    2019
    Permanent link to this record
    http://hdl.handle.net/10754/661374
    
    Metadata
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    Abstract
    Cellular Electron Cryo Tomography (CECT) 3D imaging has permitted biomedical community to study macromolecule structures inside single cells with deep learning approaches. Many deep learning-based methods have since been developed to classify macromolecule structures from tomograms with high accuracy. However, several recent studies have demonstrated the lack of robustness in these models against often-imperceptible, designed changes of input. Therefore, making existing subtomogram-classification models robust remains a serious challenge. In this paper, we study the robustness of the state-of-the-art subtomogram classifier on CECT images and propose a method called Regularized Adversarial Training (RAT) to defend the classifier against a wide range of designed threats. Our results show that RAT improves robustness for CECT image classification over the previous methods.
    Citation
    Wu, X., Mao, Y., Wang, H., Zeng, X., Gao, X., Xing, E. P., & Xu, M. (2019). Regularized Adversarial Training (RAT) for Robust Cellular Electron Cryo Tomograms Classification. 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). doi:10.1109/bibm47256.2019.8982954
    Sponsors
    This work was supported in part by U.S. National Institutes of Health (NIH) grant P41 GM103712. HW was supported by the National Institutes of Health grants R01-GM093156 and P30-DA035778. XZ was supported by a fellowship from Carnegie Mellon University’s Center for Machine Learning and Health. XG was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. BAS/1/1624, FCC/1/1976-18, FCC/1/1976-23, FCC/1/1976-25, and FCC/1/1976-26.
    Publisher
    IEEE
    Conference/Event name
    2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
    ISBN
    9781728118673
    DOI
    10.1109/BIBM47256.2019.8982954
    Additional Links
    https://ieeexplore.ieee.org/document/8982954/
    ae974a485f413a2113503eed53cd6c53
    10.1109/BIBM47256.2019.8982954
    Scopus Count
    Collections
    Conference Papers; Structural and Functional Bioinformatics Group; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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