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Found 21 papers, 2 papers with code
SHREC 2021: Classification in cryo-electron tomograms
To promote innovation in computational methods, we generate a novel simulated dataset to benchmark different methods of localization and classification of biological macromolecules in tomograms.
Harmony: A Generic Unsupervised Approach for Disentangling Semantic Content From Parameterized Transformations
Harmony leverages a simple cross-contrastive learning framework with multiple explicitly parameterized latent representations to disentangle content from transformations.
Boosting Active Learning via Improving Test Performance
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.
Cryo-shift: Reducing domain shift in cryo-electron subtomograms with unsupervised domain adaptation and randomization
Cryo-Electron Tomography (cryo-ET) is a 3D imaging technology that enables the visualization of subcellular structures in situ at near-atomic resolution.
Disentangling semantic features of macromolecules in Cryo-Electron Tomography
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.
Active Learning to Classify Macromolecular Structures in situ for Less Supervision in Cryo-Electron Tomography
Deep learning based subtomogram classification have played critical roles for such tasks.
End-to-End Robust Joint Unsupervised Image Alignment and Clustering
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.
Weakly Supervised 3D Semantic Segmentation Using Cross-Image Consensus and Inter-Voxel Affinity Relations
We propose a novel weakly supervised approach for 3D semantic segmentation on volumetric images.
Feedback-Based Dynamic Feature Selection for Constrained Continuous Data Acquisition
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.
Few shot domain adaptation for in situ macromolecule structural classification in cryo-electron tomograms
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.
Gum-Net: Unsupervised Geometric Matching for Fast and Accurate 3D Subtomogram Image Alignment and Averaging
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.
AITom: Open-source AI platform for cryo-electron tomography data analysis
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.
CS Sparse K-means: An Algorithm for Cluster-Specific Feature Selection in High-Dimensional Clustering
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.
Deep Learning-Based Strategy for Macromolecules Classification with Imbalanced Data from Cellular Electron Cryotomography
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.
Multi-task Learning for Macromolecule Classification, Segmentation and Coarse Structural Recovery in Cryo-Tomography
Cellular Electron Cryo-Tomography (CECT) is a powerful 3D imaging tool for studying the native structure and organization of macromolecules inside single cells.
Image-derived generative modeling of pseudo-macromolecular structures - towards the statistical assessment of Electron CryoTomography template matching
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.
An integration of fast alignment and maximum-likelihood methods for electron subtomogram averaging and classification
Existing subtomogram alignment based methods are prone to the missing wedge effects and low signal-to-noise ratio (SNR).
Deep learning based supervised semantic segmentation of Electron Cryo-Subtomograms
Cellular Electron Cryo-Tomography (CECT) is a powerful imaging technique for the 3D visualization of cellular structure and organization at submolecular resolution.
Feature Decomposition Based Saliency Detection in Electron Cryo-Tomograms
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.
Model compression for faster structural separation of macromolecules captured by Cellular Electron Cryo-Tomography
Electron Cryo-Tomography (ECT) enables 3D visualization of macromolecule structure inside single cells.
A convolutional autoencoder approach for mining features in cellular electron cryo-tomograms and weakly supervised coarse segmentation
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
Weakly-Supervised Semantic Segmentation
