Search Results for author: Xiangrui Zeng

Found 20 papers, 2 papers with code

Boosting Active Learning via Improving Test Performance

1 code implementation10 Dec 2021 Tianyang Wang, Xingjian Li, Pengkun Yang, Guosheng Hu, Xiangrui Zeng, Siyu Huang, Cheng-Zhong Xu, Min Xu

In this work, we explore such an impact by theoretically proving that selecting unlabeled data of higher gradient norm leads to a lower upper-bound of test loss, resulting in better test performance.

Active Learning Electron Tomography +2

Disentangling semantic features of macromolecules in Cryo-Electron Tomography

no code implementations27 Jun 2021 Kai Yi, Jianye Pang, Yungeng Zhang, Xiangrui Zeng, Min Xu

Cryo-electron tomography (Cryo-ET) is a 3D imaging technique that enables the systemic study of shape, abundance, and distribution of macromolecular structures in single cells in near-atomic resolution.

Electron Tomography

End-to-End Robust Joint Unsupervised Image Alignment and Clustering

no code implementations ICCV 2021 Xiangrui Zeng, Gregory Howe, Min Xu

To our knowledge, Jim-Net is the first end-to-end model that can simultaneously align and cluster images, which significantly improves the performance as compared to performing each task alone.

Electron Tomography

Feedback-Based Dynamic Feature Selection for Constrained Continuous Data Acquisition

no code implementations10 Nov 2020 Alp Sahin, Xiangrui Zeng

For machine learning applications on dynamic systems equipped with a large number of sensors, such as connected vehicles and robots, how to find relevant and high-quality data features in an efficient way is a challenging problem.

feature selection

Few shot domain adaptation for in situ macromolecule structural classification in cryo-electron tomograms

no code implementations30 Jul 2020 Liangyong Yu, Ran Li, Xiangrui Zeng, Hongyi Wang, Jie Jin, Ge Yang, Rui Jiang, Min Xu

Motivation: Cryo-Electron Tomography (cryo-ET) visualizes structure and spatial organization of macromolecules and their interactions with other subcellular components inside single cells in the close-to-native state at sub-molecular resolution.

Classification Domain Adaptation +2

Gum-Net: Unsupervised Geometric Matching for Fast and Accurate 3D Subtomogram Image Alignment and Averaging

no code implementations CVPR 2020 Xiangrui Zeng, Min Xu

We propose a Geometric unsupervised matching Net-work (Gum-Net) for finding the geometric correspondence between two images with application to 3D subtomogram alignment and averaging.

Electron Tomography Geometric Matching

AITom: Open-source AI platform for cryo-electron tomography data analysis

1 code implementation8 Nov 2019 Xiangrui Zeng, Min Xu

Cryo-electron tomography (cryo-ET) is an emerging technology for the 3D visualization of structural organizations and interactions of subcellular components at near-native state and sub-molecular resolution.

Electron Tomography

CS Sparse K-means: An Algorithm for Cluster-Specific Feature Selection in High-Dimensional Clustering

no code implementations26 Sep 2019 Xiangrui Zeng, Hongyu Zheng

In this paper, we propose a K-means based clustering algorithm that discovers informative features as well as which cluster pairs are separable by each selected features.

feature selection

Deep Learning-Based Strategy for Macromolecules Classification with Imbalanced Data from Cellular Electron Cryotomography

no code implementations27 Aug 2019 Ziqian Luo, Xiangrui Zeng, Zhipeng Bao, Min Xu

Deep learning model trained by imbalanced data may not work satisfactorily since it could be determined by major classes and thus may ignore the classes with small amount of data.

Classification Electron Tomography +1

Multi-task Learning for Macromolecule Classification, Segmentation and Coarse Structural Recovery in Cryo-Tomography

no code implementations16 May 2018 Chang Liu, Xiangrui Zeng, Kaiwen Wang, Qiang Guo, Min Xu

Cellular Electron Cryo-Tomography (CECT) is a powerful 3D imaging tool for studying the native structure and organization of macromolecules inside single cells.

Classification General Classification +2

Image-derived generative modeling of pseudo-macromolecular structures - towards the statistical assessment of Electron CryoTomography template matching

no code implementations12 May 2018 Kai Wen Wang, Xiangrui Zeng, Xiaodan Liang, Zhiguang Huo, Eric P. Xing, Min Xu

Cellular Electron CryoTomography (CECT) is a 3D imaging technique that captures information about the structure and spatial organization of macromolecular complexes within single cells, in near-native state and at sub-molecular resolution.

Template Matching Two-sample testing

An integration of fast alignment and maximum-likelihood methods for electron subtomogram averaging and classification

no code implementations4 Apr 2018 Yixiu Zhao, Xiangrui Zeng, Qiang Guo, Min Xu

Existing subtomogram alignment based methods are prone to the missing wedge effects and low signal-to-noise ratio (SNR).

General Classification

Deep learning based supervised semantic segmentation of Electron Cryo-Subtomograms

no code implementations12 Feb 2018 Chang Liu, Xiangrui Zeng, Ruogu Lin, Xiaodan Liang, Zachary Freyberg, Eric Xing, Min Xu

Cellular Electron Cryo-Tomography (CECT) is a powerful imaging technique for the 3D visualization of cellular structure and organization at submolecular resolution.

Semantic Segmentation

Feature Decomposition Based Saliency Detection in Electron Cryo-Tomograms

no code implementations31 Jan 2018 Bo Zhou, Qiang Guo, Xiangrui Zeng, Min Xu

To complement and speed up existing segmentation methods, it is desirable to develop a generic cell component segmentation method that is 1) not specific to particular types of cellular components, 2) able to segment unknown cellular components, 3) fully unsupervised and does not rely on the availability of training data.

Saliency Detection

A convolutional autoencoder approach for mining features in cellular electron cryo-tomograms and weakly supervised coarse segmentation

no code implementations15 Jun 2017 Xiangrui Zeng, Miguel Ricardo Leung, Tzviya Zeev-Ben-Mordehai, Min Xu

We demonstrate that the autoencoder can be used for efficient and coarse characterization of features of macromolecular complexes and surfaces, such as membranes.

Weakly-Supervised Semantic Segmentation

Cannot find the paper you are looking for? You can Submit a new open access paper.