402 citations · 923 across the 41 of their papers we have counts for
10 papers · 1 filter
Cascade Detector Analysis and Application to Biomedical Microscopy
Thomas L. Athey, Shashata Sawmya, Nir Shavit
As both computer vision models and biomedical datasets grow in size, there is an increasing need for efficient inference algorithms. We utilize cascade detectors to efficiently ide…
NeuroADDA: Active Discriminative Domain Adaptation in Connectomic
Shashata Sawmya, Thomas L. Athey, Gwyneth Liu +1
Training segmentation models from scratch has been the standard approach for new electron microscopy connectomics datasets. However, leveraging pretrained models from existing data…
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…
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…
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…
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,…
Generative Compression
Shibani Santurkar, David Budden, Nir Shavit
Traditional image and video compression algorithms rely on hand-crafted encoder/decoder pairs (codecs) that lack adaptability and are agnostic to the data being compressed. Here we…
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…
Deep Tensor Convolution on Multicores
David Budden, Alexander Matveev, Shibani Santurkar +2
Deep convolutional neural networks (ConvNets) of 3-dimensional kernels allow joint modeling of spatiotemporal features. These networks have improved performance of video and volume…