Regularized Adversarial Training (RAT) for Robust Cellular Electron Cryo Tomograms Classification
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Type
Conference PaperKAUST 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/1/1976-18FCC/1/1976-23
FCC/1/1976-26
BAS/1/1624
FCC/1/1976-25
Date
2020-02-07Submitted Date
2019Permanent link to this record
http://hdl.handle.net/10754/661374
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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/bibm47256.2019.8982954Sponsors
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
IEEEConference/Event name
2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019ISBN
9781728118673Additional Links
https://ieeexplore.ieee.org/document/8982954/ae974a485f413a2113503eed53cd6c53
10.1109/BIBM47256.2019.8982954