224 citations · 592 across the 25 of their papers we have counts for
9 papers · 1 filter
Learning and Segmenting Dense Voxel Embeddings for 3D Neuron Reconstruction
Kisuk Lee, Ran Lu, Kyle Luther +1
We show dense voxel embeddings learned via deep metric learning can be employed to produce a highly accurate segmentation of neurons from 3D electron microscopy images. A "metric g…
Chunkflow: Distributed Hybrid Cloud Processing of Large 3D Images by Convolutional Nets
Jingpeng Wu, William M. Silversmith, Kisuk Lee +1
It is now common to process volumetric biomedical images using 3D Convolutional Networks (ConvNets). This can be challenging for the teravoxel and even petavoxel images that are be…
Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy
Kisuk Lee, Nicholas Turner, Thomas Macrina +3
Neural circuits can be reconstructed from brain images acquired by serial section electron microscopy. Image analysis has been performed by manual labor for half a century, and eff…
Siamese Encoding and Alignment by Multiscale Learning with Self-Supervision
Eric Mitchell, Stefan Keselj, Sergiy Popovych +2
We propose a method of aligning a source image to a target image, where the transform is specified by a dense vector field. The two images are encoded as feature hierarchies by sia…
Synaptic Partner Assignment Using Attentional Voxel Association Networks
Nicholas Turner, Kisuk Lee, Ran Lu +3
Connectomics aims to recover a complete set of synaptic connections within a dataset imaged by volume electron microscopy. Many systems have been proposed for locating synapses, an…
PZnet: Efficient 3D ConvNet Inference on Manycore CPUs
Sergiy Popovych, Davit Buniatyan, Aleksandar Zlateski +2
Convolutional nets have been shown to achieve state-of-the-art accuracy in many biomedical image analysis tasks. Many tasks within biomedical analysis domain involve analyzing volu…
Reconstructing neuronal anatomy from whole-brain images
James Gornet, Kannan Umadevi Venkataraju, Arun Narasimhan +5
Reconstructing multiple molecularly defined neurons from individual brains and across multiple brain regions can reveal organizational principles of the nervous system. However, hi…
Convergence of gradient descent-ascent analyzed as a Newtonian dynamical system with dissipation
H. Sebastian Seung
A dynamical system is defined in terms of the gradient of a payoff function. Dynamical variables are of two types, ascent and descent. The ascent variables move in the direction of…
Sample Variance Decay in Randomly Initialized ReLU Networks
Kyle Luther, H. Sebastian Seung
Before training a neural net, a classic rule of thumb is to randomly initialize the weights so the variance of activations is preserved across layers. This is traditionally interpr…