40 citations · 76 across the 9 of their papers we have counts for
9 papers
Towards 1000-fold Electron Microscopy Image Compression for Connectomics via VQ-VAE with Transformer Prior
Fuming Yang, Yicong Li, Hanspeter Pfister +2
Petascale electron microscopy (EM) datasets push storage, transfer, and downstream analysis toward their current limits. We present a vector-quantized variational autoencoder-based…
Budgeted Broadcast: An Activity-Dependent Pruning Rule for Neural Network Efficiency
Yaron Meirovitch, Fuming Yang, Jeff Lichtman +1
Most pruning methods remove parameters ranked by impact on loss (e.g., magnitude or gradient). We propose Budgeted Broadcast (BB), which gives each unit a local traffic budget (the…
Sparse Graph Reconstruction and Seriation for Large-Scale Image Stacks
Fuming Yang, Yaron Meirovitch, Jeff W. Lichtman
We study recovering a 1D order from a noisy, locally sampled pairwise comparison matrix under a tight query budget. We recast the task as reconstructing a sparse, noisy line graph…
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…
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…
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…
Morphological Error Detection in 3D Segmentations
David Rolnick, Yaron Meirovitch, Toufiq Parag +5
Deep learning algorithms for connectomics rely upon localized classification, rather than overall morphology. This leads to a high incidence of erroneously merged objects. Humans,…
Toward Streaming Synapse Detection with Compositional ConvNets
Shibani Santurkar, David Budden, Alexander Matveev +4
Connectomics is an emerging field in neuroscience that aims to reconstruct the 3-dimensional morphology of neurons from electron microscopy (EM) images. Recent studies have success…
A Multi-Pass Approach to Large-Scale Connectomics
Yaron Meirovitch, Alexander Matveev, Hayk Saribekyan +8
The field of connectomics faces unprecedented "big data" challenges. To reconstruct neuronal connectivity, automated pixel-level segmentation is required for petabytes of streaming…