Search Results for author:
Found 7 papers, 1 papers with code
RCNN-SliceNet: A Slice and Cluster Approach for Nuclei Centroid Detection in Three-Dimensional Fluorescence Microscopy Images
Robust and accurate nuclei centroid detection is important for the understanding of biological structures in fluorescence microscopy images.
RETHINKING LOCAL LOW RANK MATRIX DETECTION:A MULTIPLE-FILTER BASED NEURAL NETWORK FRAMEWORK
In this work, we first organize the general MLLRR problem into three subproblems based on different low rank properties , and we argue that most of existing efforts focus on only one category, which leaves the other two unsolved.
Low-Rank Reorganization via Proportional Hazards Non-negative Matrix Factorization Unveils Survival Associated Gene Clusters
Towards the goal of precision health and cancer treatments, the proposed algorithm can help understand and interpret high-dimensional heterogeneous genomics data with accurate identification of survival-associated gene clusters.
Center-Extraction-Based Three Dimensional Nuclei Instance Segmentation of Fluorescence Microscopy Images
Fluorescence microscopy is an essential tool for the analysis of 3D subcellular structures in tissue.
Three dimensional blind image deconvolution for fluorescence microscopy using generative adversarial networks
Due to image blurring image deconvolution is often used for studying biological structures in fluorescence microscopy.
Tubule segmentation of fluorescence microscopy images based on convolutional neural networks with inhomogeneity correction
The segmentation results of the proposed method are visually and numerically compared with other microscopy segmentation methods.
Three Dimensional Fluorescence Microscopy Image Synthesis and Segmentation
Advances in fluorescence microscopy enable acquisition of 3D image volumes with better image quality and deeper penetration into tissue.
