Regularized Adversarial Training (RAT) for Robust Cellular Electron Cryo Tomograms Classification
KAUST DepartmentComputer Science Program
Computational Bioscience Research Center (CBRC)
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/661374
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AbstractCellular 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.
CitationWu, 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
SponsorsThis 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.
Conference/Event name2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019