Regularized Adversarial Training (RAT) for Robust Cellular Electron Cryo Tomograms Classification Cellular Electron Cryo Tomography (CECT) 3D imaging has permitted biomedical community to study macromolecule structures inside single cells with deep learning approaches. Many deep learningbased 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 oftenimperceptible, designed changes of input. Therefore, making existing subtomogramclassification models robust remains a serious challenge. In this paper, we study the robustness of the stateoftheart 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.

Domain Randomization for Macromolecule Structure Classification and Segmentation in Electron Cyrotomograms It is crucial to study and understand cellular processes. In recent years, Cellular Electron CryoTomography (CECT) serves as a powerful 3D imaging tool to visualize spatial structure of macromolecules inside the cell. However, it is challenging to analyze the macromolecular structures in a systematic way due to nature of the structural complexity of subcellular components. Existing computational and deep learning based approaches suffer from limited scalability, discrimination ability and lack of accurate annotated CECT data. Training with cheap simulated data can alleviate this problem while facing new challenges of bridging the xe2x80x9creality gapxe2x80x9d between synthetic training data and real testing data. In this paper, we tackle the tasks of macromolecule structure classification and segmentation in CECT images by adapting a simple but effective technique, domain randomization. We show that by combining deep neural models and domain randomization, we are able to achieve significant improvements of 35.21% and 46.34% in tasks of classification and semantic segmentation for real CECT data, comparing to the model trained only on syhthetic data that aims to faithfully reproduce realworld data distribution.