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

Xindi Wu, Yijun Mao, Haohan Wang, Xiangrui Zeng, Xin Gao, Eric P. Xing, Min Xu

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

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.
Original languageEnglish (US)
Title of host publication2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
PublisherIEEE
Pages52-67
Number of pages16
ISBN (Print)9781728118673
DOIs
StatePublished - Feb 7 2020

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