7 citations · 18 across the 8 of their papers we have counts for
8 papers
X-Ray2EM: Uncertainty-Aware Cross-Modality Image Reconstruction from X-Ray to Electron Microscopy in Connectomics
Yicong Li, Yaron Meirovitch, Aaron T. Kuan +5
Comprehensive, synapse-resolution imaging of the brain will be crucial for understanding neuronal computations and function. In connectomics, this has been the sole purview of volu…
The XPRESS Challenge: Xray Projectomic Reconstruction -- Extracting Segmentation with Skeletons
Tri Nguyen, Mukul Narwani, Mark Larson +9
The wiring and connectivity of neurons form a structural basis for the function of the nervous system. Advances in volume electron microscopy (EM) and image segmentation have enabl…
Subgraph Frequency Distribution Estimation using Graph Neural Networks
Zhongren Chen, Xinyue Xu, Shengyi Jiang +2
Small subgraphs (graphlets) are important features to describe fundamental units of a large network. The calculation of the subgraph frequency distributions has a wide application…
Revisiting Latent-Space Interpolation via a Quantitative Evaluation Framework
Lu Mi, Tianxing He, Core Francisco Park +3
Latent-space interpolation is commonly used to demonstrate the generalization ability of deep latent variable models. Various algorithms have been proposed to calculate the best tr…
HDMapGen: A Hierarchical Graph Generative Model of High Definition Maps
Lu Mi, Hang Zhao, Charlie Nash +7
High Definition (HD) maps are maps with precise definitions of road lanes with rich semantics of the traffic rules. They are critical for several key stages in an autonomous drivin…
Learning Guided Electron Microscopy with Active Acquisition
Lu Mi, Hao Wang, Yaron Meirovitch +5
Single-beam scanning electron microscopes (SEM) are widely used to acquire massive data sets for biomedical study, material analysis, and fabrication inspection. Datasets are typic…
Training-Free Uncertainty Estimation for Dense Regression: Sensitivity as a Surrogate
Lu Mi, Hao Wang, Yonglong Tian +2
Uncertainty estimation is an essential step in the evaluation of the robustness for deep learning models in computer vision, especially when applied in risk-sensitive areas. Howeve…
Cross-Classification Clustering: An Efficient Multi-Object Tracking Technique for 3-D Instance Segmentation in Connectomics
Yaron Meirovitch, Lu Mi, Hayk Saribekyan +3
Pixel-accurate tracking of objects is a key element in many computer vision applications, often solved by iterated individual object tracking or instance segmentation followed by o…