Search Results for author:
Found 9 papers, 4 papers with code
Simple but Effective Unsupervised Classification for Specified Domain Images: A Case Study on Fungi Images
Addressing this, an unsupervised classification method with three key ideas is introduced: 1) dual-step feature dimensionality reduction using a pre-trained model and manifold learning, 2) a voting mechanism from multiple clustering algorithms, and 3) post-hoc instead of prior manual annotation.
CryoAlign: feature-based method for global and local 3D alignment of EM density maps
Advances on cryo-electron imaging technologies have led to a rapidly increasing number of density maps.
MFAI: A Scalable Bayesian Matrix Factorization Approach to Leveraging Auxiliary Information
Here, we consider a matrix factorization problem by utilizing auxiliary information, which is massively available in real-world applications, to overcome the challenges caused by poor data quality.
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.
Self-Supervised Cryo-Electron Tomography Volumetric Image Restoration From Single Noisy Volume With Sparsity Constraint
The proposed method only requires a single noisy data as training input and no ground-truth is needed in the whole training procedure.
A Hybrid Frequency-Spatial Domain Model for Sparse Image Reconstruction in Scanning Transmission Electron Microscopy
Scanning transmission electron microscopy (STEM) is a powerful technique in high-resolution atomic imaging of materials.
DWMD: Dimensional Weighted Orderwise Moment Discrepancy for Domain-specific Hidden Representation Matching
Our metric function takes advantage of a series for high-order moment alignment, and we theoretically prove that our DWMD metric function is error-free, which means that it can strictly reflect the distribution differences between domains and is valid without any feature distribution assumption.
SegET: Deep Neural Network with Rich Contextual Features for Cellular Structures Segmentation in Electron Tomography Image
Electron tomography (ET) allows high-resolution reconstructions of macromolecular complexes at nearnative state.
DLBI: Deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopy
Our method combines the strength of deep learning and statistical inference, where deep learning captures the underlying distribution of the fluorophores that are consistent with the observed time-series fluorescent images by exploring local features and correlation along time-axis, and statistical inference further refines the ultrastructure extracted by deep learning and endues physical meaning to the final image.
